ArticleInvisible Digital FrontCan Cyber Attacks Shape.docx

Article
Invisible Digital Front:
Can Cyber Attacks Shape
Battlefield Events?
Nadiya Kostyuk
1
, and Yuri M. Zhukov
1
Abstract
Recent years have seen growing concern over the use of cyber
attacks in wartime,
but little evidence that these new tools of coercion can change
battlefield events.
We present the first quantitative analysis of the relationship
between cyber activities
and physical violence during war. Using new event data from
the armed conflict in
Ukraine—and additional data from Syria’s civil war—we
analyze the dynamics of
cyber attacks and find that such activities have had little or no
impact on fighting. In
Ukraine—one of the first armed conflicts where both sides
deployed such tools
extensively—cyber activities failed to compel discernible
changes in battlefield
behavior. Indeed, hackers on both sides have had difficulty
responding to battlefield
events, much less shaping them. An analysis of conflict

dynamics in Syria produces
similar results: the timing of cyber actions is independent of
fighting on the ground.
Our finding—that cyber attacks are not (yet) effective as tools
of coercion in war—
has potentially significant implications for other armed
conflicts with a digital front.
Keywords
compellence, coercion, physical violence, conflict, cyber
attacks
On December 23, 2015, hackers attacked Ukraine’s power grid,
disabling control
systems used to coordinate remote electrical substations, and
leaving people in the
capital and western part of the country without power for
several hours. The Security
1Department of Political Science, University of Michigan, Ann
Arbor, MI, USA
Corresponding Author:
Nadiya Kostyuk, Department of Political Science, University of
Michigan, 505 S State Street, Ann Arbor,
MI 48109, USA.
Email: [email protected]
Journal of Conflict Resolution
2019, Vol. 63(2) 317-347
ª The Author(s) 2017

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Service of Ukraine (SBU) blamed the Russian government for
the cyber attack, an
accusation that later found support in malware analysis by a
private computer
security firm. The Ukrainian hack was the first publicly
acknowledged case of a
cyber attack successfully causing a power outage. It is also just
one of thousands of
cyber activities, mostly diffuse and low level, that have
occurred alongside physical
fighting in Ukraine. Attacks launched through the digital realm
are playing an
increasingly visible role in civil and interstate conflict—in
Ukraine, Syria, Israel,
Estonia, Georgia, and beyond. Yet it remains unknown whether

such activities have
a real coercive impact on the battlefield.
1
Recent years have seen growing concern over the coercive
potential of cyber
capabilities in war, but little evidence that these new tools are
yet making a differ-
ence. Theoretically, most research has focused on the
consequences of cyber attacks
for peacetime deterrence rather than wartime compellence
(Libicki 2009; Sharma
2010; Andres 2012).
2
Yet the logic of coercion entails distinct challenges in peace
and war, with potentially different implications for the cyber
domain. Empirically,
the literature has relied more on qualitative case studies than
quantitative data. The
few data sets that do exist (Valeriano and Maness 2014)
privilege massive cyber
catastrophes over less sophisticated low-intensity attacks, like
distributed denial of
service (DDoS). The latter category, however, is far more
common.

This article asks whether cyber attacks can compel short-term
changes in battle-
field behavior, using new event data on cyber and kinetic
operations from armed
conflicts in Ukraine and Syria. We use the Ukrainian conflict as
our primary test case
due to the extensive and sophisticated use of cyber attacks by
both sides (Geers
2015), and—uniquely—overt claims of responsibility, public
damage assessments,
and other releases of information that reduce uncertainty over
timing and attribution.
Since 2014, Ukraine has turned into “a training playground for
research and devel-
opment of novel attack techniques” (Zetter 2017). If cyber
attacks can yet make a
difference on the battlefield, Ukraine is one a few cases where
we are most likely to
observe such an effect. Our data include 1,841 unique cyber
attacks and 26,289
kinetic operations by government and prorebel forces between
2014 and 2016. We
supplement this quantitative analysis with fourteen primary
source interviews with

participants in the cyber campaign as well as Ukrainian,
Russian, and Western cyber
security experts with direct knowledge of these operations.
To evaluate the generalizability of the Ukrainian experience to
other conflicts, we
replicate our results with data from Syria’s civil war. Like
Ukraine, Syria has seen
the extensive use of low-level cyber attacks by factions fighting
for and against the
incumbent regime. Because this war has gone on significantly
longer than the
conflict in Ukraine—giving hackers more time to organize and
develop their cap-
abilities—Syria offers a glimpse at cyber activities in a more
protracted, higher
intensity context. If we uncover similar patterns in two conflicts
of such different
scale and complexity, we can have greater confidence that our
results are not arti-
facts of a single idiosyncratic case. Our data include 682 cyber
attacks and 9,282 acts
of violence by pro- and anti-Assad forces between 2011 and
2016.

318 Journal of Conflict Resolution 63(2)
Evidence from both conflicts suggests that cyber attacks have
not created forms
of harm and coercion that visibly affect their targets’ actions.
Short of mounting
synchronized, coordinated cyber campaigns, each group of
hackers has seemed to
operate in its own “bubble,” disengaged from unfolding events
in both cyberspace
and the physical world. The lack of discernible reciprocity
between cyber and kinetic
operations—and between the cyber actors themselves—
questions whether cyber
attacks can (yet) be successfully deployed in support of military
operations.
This disconnect may be temporary, as joint planning and
execution concepts
continue to evolve. Many countries, for instance, still struggle
in coordinating air-
power for ground combat support, a century after World War I.
Our study highlights
some of the difficulties that countries will need to overcome in
integrating and

synchronizing these new capabilities.
Our contribution is fourfold. We offer the first disaggregated
analysis of cyber
activities in war and take stock of the empirical relationship
between the cyber and
kinetic dimensions of modern battle. To do so, we collect the
first microlevel data on
wartime cyber attacks, using both open media sources and
anonymous attack traffic
data. Theoretically, our analysis addresses an important
question on the coercive
impact of low-level cyber attacks, advancing a literature that
has been heavy on
deductive argumentation, but light on evidence. Finally, from a
policy standpoint,
our findings should temper the popular tendency to overhype
the transformative
potential of cyber attacks. At present, interaction between cyber
and kinetic opera-
tions is similar to that between airpower and ground operations
in World War I—
when armies began to use aircraft for reconnaissance but had
not realized their full

potential to shape battlefield outcomes.
Varieties of Cyber Activity
The term “cyber activities” captures a diverse assortment of
tactics and procedures,
directed against different types of targets, in pursuit of
disparate objectives. Not all
of these activities seek to achieve battlefield effects in the same
way. Before pro-
ceeding further, we differentiate between two broad goals these
actions tend to
pursue: propaganda and disruption.
3
Cyber activities in the propaganda category seek to influence
public opinion
and indirectly undermine an opponent’s financing or
recruitment. Operations in
this group include leaks of compromising private information,
online publication
of partisan content (e.g., “trolling” on comments pages), and the
establishment of
dedicated websites and forums to promote an armed group’s
message. Unless it
openly incites or discourages violence, propaganda affects
kinetic operations only

indirectly by undermining an opponent’s support base or
obfuscating perceptions
of events.
In the Ukrainian conflict, the importance of both groups attach
to online propa-
ganda is evident from the time and resources pro-Kyiv fighters
spend updating
Wikipedia, and pro-Russia groups devote to creating and
running dedicated
Kostyuk and Zhukov 319
YouTube channels and social media accounts. Russian military
doctrine places a
heavy emphasis on the strategic use of information in warfare,
as does US cyber-
space joint planning doctrine.
The second category of cyber attacks—disruption—seeks to
directly sabotage
opponents’ ability to operate in the physical or electronic realm.
These mostly low-
intensity activities include denial of service attacks, which
make targeted resources

unavailable through a flood of requests from a single source,
and DDoS attacks,
where requests originate from multiple compromised systems.
Related efforts
include inundating communications systems with floods of text
messages or phone
calls and using fire walls and proxies to block access to
websites. At the extreme end
of the scale is the use of malicious code to inflict physical
damage or otherwise
compromise infrastructure and military objects. Examples
include interception of
drones, communications and surveillance systems, control of
Wi-Fi access points,
and collection of protected information via phishing.
The most sophisticated known attack of this type is the Stuxnet
worm,
which—before its discovery in 2010—targeted industrial control
systems critical
to uranium enrichment in Iran. In Ukraine, notable disruptive
activities have
included attacks on the Central Election Committee’s website
during the 2014
presidential elections and attacks on the country’s power grid in

2015 and 2016.
Other examples include the use of malware to collect
operational intelligence,
like X-Agent, which retrieved locational data from mobile
devices used by Ukrai-
nian artillery troops, and the hacking of closed-circuit
television (CCTV) cameras
behind enemy lines.
Propaganda and disruption are not mutually exclusive, and
many cyber activities
serve both purposes—shaping public opinion through disruption
or disrupting an
opponent’s operations by shaping public opinion. For example,
altering the visual
appearance of websites can have the dual effect of embarrassing
the target and
limiting its ability to communicate. Leaks of private
information also have dual
implications for targets’ public image and physical security.
Recent examples of hybrid activities include the defacement of
US Central
Command’s Twitter and Facebook pages by the Islamic State’s
(IS) Cyber Caliphate

and operations by US Cyber Command against IS beginning in
April 2016. In
Ukraine, the pro-rebel group CyberBerkut (CB) has leaked
private communications
from senior United States, European Union, and Ukrainian
officials and disclosed
identities of pro-Kyiv field commanders—simultaneously
creating a media scandal
and forcing targets to commit more resources to personal
security. Similarly, the
pro-Kyiv website Myrotvorets’ published names and addresses
of suspected “rebel
sympathizers”—information that allegedly facilitated several
assassinations
(Il’chenko 2016).
In the following, we limit the scope of our inquiry to cyber
actions that are either
purely disruptive (e.g., DDoS-style attacks) or are hybrids of
the two approaches
(e.g., web defacements). We do so for two reasons. First, most
purely propagandistic
operations, like comment-board trolling, do not aspire to
influence the course of

military operations in the short term. Second, it is hard to
separate the disruptive and
propaganda effects of hybrid cyber activities because they
depend on each other.
Cyber Coercion in Wartime
Over the last two decades, cyber attacks have become an
increasingly common tool
of coercion, used by state and nonstate actors, independently
and jointly with phys-
ical, kinetic operations. Like other instruments of coercion,
cyber actions inflict
costs on a target to compel a change in its behavior—either by
punishing past
misdeeds or by putting pressure on decision makers in real time.
The role of cyber compellence in wartime is not unlike that of
airpower or
terrorism (Pape 2003, 2014). Cyber attacks cannot take or hold
territory on their
own, but they can support operations on the ground by
disrupting opponents’ com-
mand and control, collecting operational intelligence, and
creating opportunities for

conventional forces to exploit. If combatants use the Internet
for coordination,
recruitment, or training, low-level cyber disruption may prevent
them from running
these vital functions smoothly.
4
Alternatively, cyber attacks can indirectly pressure
an opponent by targeting civilian economy and infrastructure,
similarly to strategic
bombing. Yet unlike airpower, an operational cyber capability
is relatively inexpen-
sive to develop. It does not require new massive infrastructure,
and many activities
can be delegated to third parties (Ottis 2010). Unlike terrorism,
the individual
attacker is rarely at risk of direct physical harm.
Despite the apparent promise of these “weapons of the future”
(Schmitt 1999;
Rios 2009; Clarke and Knake 2010; McGraw 2013; Eun and
Aßmann 2014), some
scholars are skeptical that low-level cyber attacks can be an
effective tool of coer-
cion (Liff 2012; Rid 2012; Gartzke 2013; Junio 2013). There is

little doubt that large
numbers of low-level attacks can cumulatively produce large-
scale damage, bring-
ing “death by a thousand cuts” (Lemay, Fernandeza, and Knight
2010). Yet suc-
cessful coercion also requires punishment to be both anticipated
and avoidable
(Schelling 1966), and these criteria can be difficult to meet in
cyberspace.
Cyber attacks can be challenging for targets to anticipate
because attackers face
strong incentives to mount surprise “zero-day” exploits, before
targets recognize and
patch their vulnerabilities (Axelrod and Iliev 2014).
5
Since the destructiveness of
malicious code depreciates quickly after first use, cyber attacks
are often most
damaging when they are least anticipated.
Targets also have many reasons to doubt that cyber attacks are
avoidable by
accommodation. For the attacker, cyber actions present a trade-
off between plausi-

ble deniability—which helps prevent retaliation—and the
credibility of coercive
promises and threats.
6
Any uncertainty over the source of an attack will also create
uncertainty over the nature of compliance—what sort of actions
will prevent future
attacks and by whom.
Beyond attribution uncertainty, cyber attacks may not generate
sufficient costs to
elicit compliance from. Because administrators can quickly fix
or contain many
exploited vulnerabilities, even successful attacks cause only
temporary disruption
(Axelrod and Iliev 2014). Unless the attacker continues to
develop new methods and
identify new vulnerabilities, a protracted campaign may quickly
lose its coercive
impact. As a result, targets may see compliance as insufficient
and unnecessary to
stop the damage (Hare 2012; Lynn 2010; Nye 2010).

Force synchronization challenges may also render the timing of
cyber attacks
suboptimal for compellence. Hackers—especially those not
integrated with military
forces—may not observe battlefield events on a tactically
relevant time line. Even if
they did, the lead time required to plan and implement a
successful attack—studying
the target system, collecting intelligence on its vulnerabilities,
and writing code that
exploits them—can make these efforts difficult to synchronize
with conventional
operations.
These challenges are not insurmountable. Lead time is a greater
barrier for high-
level attacks (e.g., targeting major infrastructure) than for more
routine, DDoS-style
attacks. Force synchronization difficulties are also not unique to
the cyber domain
and are well established in research on terrorism and airpower
(Atran 2003; Pape
2003, 2014). The ability of contemporary hackers to overcome
these difficulties,

however, remains unknown.
Previous Research
The question of whether low-level cyber attacks compel has
deep implications for
the theory and practice of national security. Yet the public and
academic debate on
this topic has unfolded largely in the absence of rigorous
empirical evidence in either
direction. Existing political science and policy literature on
cybersecurity could be
grouped into three broad areas: the “big picture” of cyber
warfare (Cha 2000;
Griniaiev 2004; Libicki 2007, 2011; Czosseck and Geers 2009;
Clarke and Knake
2010; Axelrod and Iliev 2014), the overlap between cyber and
kinetic capabilities
(Healey 2013; Kello 2013; Libicki 2015; Andress and
Winterfeld 2013; Axelrod
2014), and the effect of information and communication
technology on conflict
(Martin-Shields 2013; Pierskalla and Hollenbach 2013;
Crabtree, Darmofal, and
Kern 2014; Gohdes 2014; Bailard 2015).

Most research in the first category has focused on the
implications of cyber
activities for peacetime deterrence or the offense–defense
balance rather than war-
time compellence. While the second group focuses more
directly on cyber attacks
during conflict, its empirical approach has been mostly
qualitative, relying on evi-
dence from descriptive case studies, macrohistorical surveys,
and stylized facts.
Some large-n analyses do exist (Valeriano and Maness 2014),
but their scope has
remained on large-scale cyber attacks rather than the far more
numerous low-
intensity operations we consider here. While the third group
does employ the
statistical analysis of disaggregated data, its theoretical scope is
distinct from main-
stream literature on cyber attacks—evaluating, for instance,
how technology affects
collective action (Weidmann 2015) rather than military
compellence.

Our study bridges the gap between these areas of inquiry. Our
goal is to assess the
coercive potential of low-level cyber actions during an armed
conflict. We pursue
this goal by studying the magnitude and direction of the
relationship between cyber
attacks and physical violence, using microlevel data from
ongoing conflicts in
Ukraine and Syria.
Empirical Expectations
Cyber attacks by actor A can affect physical violence by B in
one of the three ways:
negatively, positively, or not at all. If cyber compellence is
successful, we should
expect a short-term decrease in violence after a spike in cyber
attacks. A positive
response would suggest failure, where cyber attacks actually
escalate violence by the
opponent. If no relationship exists, cyber actions are either
ineffective or irrelevant
to fighting in the physical world.
In addition to compellence across domains, cyber attacks by
actor A may impact

cyber attacks by actor B. As before, only a negative relationship
would imply
coercive success, while a null or positive response would
suggest that these actions
are either ineffective or counterproductive.
Data Analysis
To evaluate whether and how cyber actions affect physical
violence in war, we
analyze new micro-level data from Ukraine and Syria. We begin
with an in-depth
study of the Ukrainian case, as one of few conflicts where both
sides have used cyber
attacks as a means of coercion. Due to the sophistication of
hackers on both sides, the
public nature of many attacks, and an abundance of data, the
Ukrainian conflict
allows us to observe the short-term coercive impact of cyber
attacks.
7
We then use
analogous event data on Syria to evaluate the generalizability of
our results. While a
more systematic analysis of cross-national patterns lies beyond

the scope of our
article, micro-level evidence from these two conflicts might be
suggestive of general
patterns of modern warfare—particularly where combatants with
asymmetric cap-
abilities use cyberspace along with traditional tools of war.
In assembling our data, we follow two general guidelines. To
address systematic
differences in event reporting cross countries and media outlets
(Baum and Zhukov
2015; Davenport and Stam 2006; Woolley 2000), we draw data
from multiple open
sources—including press reports and anonymous attack traffic
data. To reduce
potential false positives, we include only those events that have
been reported by
more than one source.
8
Ukraine Cyber Attacks Data
Our cyber event data on Ukraine include 1,841 unique, mostly
low-level, cyber
attacks from August 27, 2013, to February 29, 2016, drawn from
two sets of sources.

First are media reports of cyber attacks from rebel, Russian,
Ukrainian, and Western
news outlets, press releases and blogs along with social media
platforms used by the
involved nonstate actors.
9
Second is the private cyber security firm Arbor Networks’
Digital Attack Map (DAM; see
http://www.digitalattackmap.com/about/). Unlike
media sources—which include only cyber attacks publicly
reported by news orga-
nizations or claimed by governments and hacker groups
directly—DAM draws on
anonymous attack traffic data and network outage reports to
enumerate the top 2
percent of reported attacks that generate unusually high Internet
traffic for each
country. Including these “higher-visibility” attacks should make
it easier to find a
coercive effect.
We supplemented these data with fourteen primary source

interviews with parti-
cipants in the cyber campaign, as well as Russian, Ukrainian,
and Western cyber
security experts with direct knowledge of these operations, from
the private and
public sectors, academia, and journalism.
10
We conducted all interviews in person or
via e-mail or Skype in the summer and fall 2015 and provide
full transcripts in the
Online Appendix (Kostyuk and Zhukov 2017).
We grouped cyber attacks in our data set according to the
partisanship of alleged
perpetrators (pro-Ukrainian vs. prorebel) and the type of
operation they conducted
(propaganda vs. disruption). Table 1 list all actors conducting
cyber activitiess in the
Ukrainian conflict, their targets, and the reported frequency of
their activities.
Ukrainian cyber actions include specific attacks by pro-Kyiv
hackers like
Anonymous Ukraine and Ukrainian Cyber Forces (UCFs). The
latter is the most

active group on the pro-Ukrainian side. In an interview, UCF
leader Eugene
Dokukin claimed to have established the nonstate group in
March 2014, in
response to Russian cyber attacks. Due to the “secret nature” of
the organization,
Dokukin was reluctant to discuss its size but noted that the
number of volunteers
fluctuates depending on the state of kinetic operations in eastern
Ukraine (Kostyuk
and Zhukov 2017, # 1). Pro-Kyiv hackers’ most common targets
are the commu-
nications and finances of rebel units as well as media firms and
private companies
in rebel-held areas.
Prorebel cyber actions include specific attacks by proseparatist
or pro-Russian
cyber actors, like CB, Cyber Riot Novorossiya, Green Dragon,
and the Russian
government. The first of these takes its name from Ukraine’s
disbanded Berkut riot
police and claims to fight “neofascism” in Ukraine. Ukrainian
and Russian cyber
experts we interviewed offered contradictory assessments on

CB’s organizational
structure. One Russian expert said that CB consists of former
SBU employees who
lost their jobs after the Euromaidan revolution (Kostyuk and
Zhukov 2017, # 12).
Contrarily, Ukrainian interviewees viewed CB either as a virtual
group controlled by
the Federal Security Service (FSB) or as a unit within the FSB
(Kostyuk and Zhukov
2017, #7 & #8). These groups’ most popular targets include
Ukrainian government
officials, media, and private citizens.
We further disaggregated these events into the two categories
previously
defined—propaganda or disruption—as well as a third, hybrid,
category of incidents
http://www.digitalattackmap.com/about/
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n
it
s
A
3
(<
1
)
Ja
b
h
at
al
-N
u
sr
a–
sp
o
n
so
re
d
u
n

it
s
A
1
(<
1
)
S
yr
ia
n
go
ve
rn
m
e
n
t
u
n
it
s
an
d
o
ff

ic
ia
ls
A
/T
2
(<
1
)/
2
7
2
(4
0
)
K
u
rd
is
h
n
o
n
-s
ta
te

o
p
p
o
si
ti
o
n
A
1
1
(<
2
)
S
yr
ia
n
st
at
e
–
sp
o
n
so

re
d
u
n
it
s
A
/T
1
0
2
(1
5
)/
2
(<
1
)
F
re
e
S
yr
ia
n
A

rm
y
T
1
(<
1
)
P
ro
-A
ss
ad
n
o
n
-s
ta
te
ac
to
rs
T
1
7
9

(2
6
)
P
ro
-I
S
IL
so
ci
al
m
e
d
ia
an
d
w
e
b
si
te
s
T
1
4
0

(2
1
)
T
o
ta
l
6
8
2
(1
0
0
)
6
8
2
(1
0
0
)
N
o
te
:
IS
IL

¼
T
h
e
Is
la
m
ic
S
ta
te
o
f
Ir
aq
an
d
th
e
L
e
va
n
t.
325

that potentially serve both purposes. The most common cyber
actions in Ukraine
have been DDoS-style attacks, followed by hacks of CCTV
cameras and other
communications. Website blockages have also proven popular,
as have spear-
phishing e-mails targeting specific individuals. Table 2 provides
a full breakdown.
To reduce false positives due to unconfirmed reports or dubious
claims of respon-
sibility, we only include attacks reported by more than one
source. To account for
uncertainty of attribution, we marked as “disputed” all cases
where no one claimed
responsibility and labeled as “nondisputed” those operations for
which actors
directly claimed responsibility in press releases, on social
media, or in interviews.
11
To focus on daily dynamics, we excluded activities whose
intensity did not vary over
time.
12

Figure 1a depicts the temporal dynamics of pro-Ukrainian
(Cyber U) and
pro-Russian rebel (Cyber R) cyber operations.
13
In early March 2014, about a
week after the revolution in Kyiv, Figure 1 shows a spike in
attacks by CB.
The same month saw the establishment of the pro-Kyiv
Ukrainian Cyber
Forces, partly in response to CB’s attacks. However, UCF
operations do not
become visible until May 2014, following an influx of
volunteers to the group.
May 2014 is also notable for a rise in activities by another pro-
Russian cyber
group, Cyber Riot Novorossiya—named after the czarist-era
term (“New
Russia”) for territories in southeastern Ukraine. After the first
Minsk cease-
fire agreement in September 2014, operations by pro-Ukrainian
hackers con-
verge to a steady rate of two to four per day, with occasional
flare-ups, as in
December 2014. Activities by pro-Russian hackers, by contrast,

declined after
the summer 2014.
Ukraine Violent Events Data
Our data on kinetic operations include 26,289 violent events
from Ukraine’s Donbas
region, recorded between February 28, 2014, and February 29,
2016. To offset
reporting biases in any one source, while guarding against
potential disruptions in
media coverage due to cyber attacks, these data draw on
seventeen Ukrainian,
Russian, rebel, and international sources.
14
As before, we include only events that
appeared in more than one source.
To extract information on dates, locations, participants, and
other event details,
we relied on a combination of supervised machine learning
(Support Vector
Machine) and dictionary-based coding. The Online Appendix
describes our mea-
surement strategy and provides summary statistics.

Figure 1b shows the temporal distribution of pro-Ukrainian
(Kinetic U) and pro-
Russian rebel (Kinetic R) physical violence. The plot shows
several notable flare-
ups of fighting—during a government offensive in late June
2014 and a rebel
offensive in January 2015—as well as lulls following cease-fire
agreements in
September 2014, February 2015, and September 2015.
Compared to the cyber
operations in Figure 1, this plot reveals a clear correlation
between kinetic operations
T
a
b
le
2
.
T
yp
e
s

o
f
C
yb
e
r
O
p
e
ra
ti
o
n
s
(U
k
ra
in
e
an
d
S
yr
ia
).
P
ro

p
ag
an
d
a
U
k
ra
in
e
(%
)
S
yr
ia
(%
)
D
is
ru
p
ti
o
n
U

k
ra
in
e
(%
)
S
yr
ia
(%
)
B
o
th
U
k
ra
in
e
(%
)
S
yr
ia
(%

)
P
P
I—
p
u
b
lis
h
in
g
o
n
lin
e
p
ri
va
te
in
fo
rm
at
io
n
o

f
th
e
m
e
m
b
e
rs
o
f
th
e
co
n
fl
ic
ti
n
g
p
ar
ti
e
s
4

7
(2
)
5
9
(9
)
A
V
G
—
au
d
io
-,
vi
d
e
o
-,
an
d
ge
o
-i
n

te
lli
ge
n
ce
co
lle
ct
io
n
4
2
3
(2
3
)
1
(<
1
)
W
D
T
—

w
e
b
si
te
d
e
fa
ce
m
e
n
t
5
1
(3
)
3
8
9
(5
7
)
P
R
M
/P

U
M
—
p
o
st
in
g
p
ro
-
re
b
e
l
an
d
p
ro
-
U
k
ra
in
ia

n
m
e
ss
ag
e
s
o
n
lin
e
5
4
(3
)/
5
(<
1
)
—
C
P
I—
co
lle

ct
in
g
p
ri
va
te
in
fo
rm
at
io
n
vi
a
o
p
e
n
so
u
rc
e
s
1
3

(<
1
)
1
0
(<
2
)
U
W
P
—
u
p
d
at
in
g
o
n
lin
e
p
ag
e
s

6
(<
1
)
—
D
D
o
S
—
d
is
tr
ib
u
te
d
d
e
n
ia
l-
o
f-
se
rv

ic
e
at
ta
ck
4
9
9
(2
7
)
7
8
(1
1
)
O
D
S
—
o
th
e
r
at

ta
ck
s
w
it
h
a
p
u
rp
o
se
o
f
d
is
ru
p
ti
o
n
o
r
e
sp
io
n

ag
e
9
(1
)
1
0
(<
2
)
S
P
E
—
sp
e
ar
-p
h
is
h
in
g
e
-m
ai

l
2
3
4
(1
3
)
1
7
(2
.5
)
S
T
M
—
se
n
d
in
g
m
as
si
ve
te

x
t
m
e
ss
ag
e
s
o
r
ca
lli
n
g
p
h
o
n
e
s
n
o
n
-s
to
p

4
0
(2
)
1
(<
1
)
W
B
G
—
w
e
b
si
te
b
lo
ck
ag
e
2
5
7
(1

4
)
3
7
6
(5
5
)
W
FC
—
ga
in
in
g
co
n
tr
o
l
o
f
W
i-
F
i
ac

ce
ss
p
o
in
ts
an
d
ch
an
gi
n
g
th
e
m
to
th
o
se
o
f
th
e
o

p
p
o
n
e
n
ts
3
1
(<
2
)
—
T
o
ta
l
1
,8
4
1
(1
0
0
)
6
8
2

(1
0
0
)
1
,8
4
1
(1
0
0
)
6
8
2
(1
0
0
)
1
,8
4
1
(1
0
0
)

6
8
2
(1
0
0
)
327
by the two sides, with government and rebel attacks rising and
falling in tandem.
15
Although this interdependence is not surprising, the data
suggest that—with few
exceptions—physical violence in Ukraine has been a reciprocal
affair.
From a brief glance at the timing of cyber and physical
operations (Figure 1a
and b), there are relatively few signs of a compellence effect—
changes in the
former do not appear to drive changes in the latter. However, a
visual compar-
ison can be misleading. Some of the variation may be due to
fighting on the

ground or in cyberspace, but other changes may reflect secular
trends or shocks
due to elections and other events not directly related to conflict.
To account for
these potential confounding factors and to gauge whether there
is a stronger
cyber–kinetic relationship than we would expect by chance, we
conduct a series
of more rigorous tests.
Figure 1. Cyber and kinetic operations in Ukraine (March 2014–
February 2016). U (blue)
indicates operations by Ukrainian government forces; R (red)
indicates operations by pro-
Russian rebel groups.
Empirical Strategy
To evaluate the relationship between cyber and kinetic
operations in Ukraine, we
estimate a series of vector autoregressive models
16
Yt ¼
Xp
j

BjYt�j þ GXt þ m0 þ m1t þ �t; ð1Þ
where Yt ¼
h
y
KineticðUÞ
t ; y
KineticðRÞ
t ; y
CyberðUÞ
t ; y
CyberðRÞ
t
i0
is a matrix of endogenous
variables, and Xt ¼ ½x1t; . . . ; xkt�
0
is a matrix of k exogenous variables, which
includes indicators for key dates and events during the war, like
presidential and
parliamentary electoral campaigns in Ukraine and breakaway
territories; cease-fire
agreements; and Ukrainian, Russian, and Soviet holidays.
Deterministic components

include a constant term (m0) and trend (m1t). p is the lag order,
selected via Bayesian
information criterion, and �t is a vector of serially uncorrelated
errors.
We control for Ukrainian, Russian, and Soviet holidays because
anecdotal
accounts suggest significant increases in cyber activity during
such times. The UCF,
for instance, had an operation called “Happy New Year,” which
sought to print pro-
Ukrainian messages from hacked printers in Crimea, Russia, and
Donbas. National
election campaigns represent another time when such activities
may spike. Before
and during the presidential elections, for instance, hackers
bombarded Ukraine’s
Central Electoral Committee website with DDoS attacks.
Finally, we may expect
cease-fire agreements aimed at reducing physical violence to
also have an effect in
the cyber domain. For example, the cyber espionage operation
“Armageddon”—
directed against Ukrainian government websites—intensified
before the Minsk I
agreement went into force but then rapidly declined.

Because we are interested in the relationship between cyber
attacks and physical
violence during war, we limit our primary analysis to the active
phase of military
operations between May 11, 2014, and February 15, 2015—the
period following
independence referendums organized by the self-proclaimed
Donetsk and Luhansk
People’s Republics and the second Minsk cease-fire agreement.
In the Online Appen-
dix, we present additional analyses of the full data set, which
produced similar results.
Results
Data from Ukraine support the skeptical view of cyber coercion.
The impulse–
response curves in Figure 2 show a strong, escalatory dynamic
between kinetic
operations by the two sides (Kinetic U, Kinetic R), but no
tangible links in either
direction between kinetic and cyber operations, and no
reciprocity between cyber
actions (Cyber U, Cyber R).
Following a standard deviation increase in kinetic rebel attacks,

government
violence sees a delayed rise, peaking around two days after the
shock and gradually
R
es
po
ns
e:
K
in
et
ic
(U
)
0
5
10
15
20
25
30
0246

0
5
10
15
20
25
30
0.00.51.01.52.0
0
5
10
15
20
25
30
−0.8−0.40.00.4
0
5
10
15
20
25
30

−1.5−1.0−0.50.0
0
5
10
15
20
25
30
0.01.02.03.0
0
5
10
15
20
25
30
0123456
0
5
10
15
20

25
30
−0.6−0.20.20.6
0
5
10
15
20
25
30
−1.0−0.50.0
0
5
10
15
20
25
30
−0.20.00.10.20.3
0
5
10

15
20
25
30
−0.10.10.3
0
5
10
15
20
25
30
0.01.02.03.0
0
5
10
15
20
25
30
−0.10.00.10.20.3
0
5

10
15
20
25
30
−0.100.000.05
0
5
10
15
20
25
30
−0.050.050.15
0
5
10
15
20
25
30
−0.06−0.020.02

0
5
10
15
20
25
30
0.00.20.4
K
in
et
ic
(R
)
C
yb
er
(U
)
C
yb
er
(R
)

Im
pu
ls
e:
K
in
et
ic
(U
)
K
in
et
ic
(R
)
C
yb
er
(U
)
C
yb
er
(R
)
F
ig
u
r

e
2
.
Im
p
u
ls
e
re
sp
o
n
se
m
at
ri
x
,
d
ai
ly
ti
m
e
se
ri
e

s
(U
k
ra
in
e
).
L
ig
h
t
gr
ay
ar
e
a
re
p
re
se
n
ts
9
5
p
e

rc
e
n
t
co
n
fi
d
e
n
ce
in
te
rv
al
s,
m
e
d
iu
m
gr
ay
9
0
p
e

rc
e
n
t,
d
ar
k
gr
ay
6
8
p
e
rc
e
n
t.
“U
”
in
d
ic
at
e
s
re

p
o
rt
e
d
k
in
e
ti
c
an
d
cy
b
e
r
o
p
e
ra
ti
o
n
s
b
y
p

ro
-U
k
ra
in
ia
n
go
ve
rn
m
e
n
t
fo
rc
e
s,
an
d
“R
”
in
d
ic

at
e
s
o
p
e
ra
ti
o
n
s
b
y
p
ro
-R
u
ss
ia
n
re
b
e
l
fo
rc
e
s.

330
declining back to zero (top row, second column). Rebel
operations also rise after
shocks to government operations (second row, first column), but
the response here is
immediate, without the delay we observe in government
operations. This pattern
may reflect command and control inefficiencies in the Ukrainian
army, particularly
early in the conflict, when indecision and leadership turnover
lengthened decision
cycles.
The relationship between cyber and kinetic operations is far
weaker than that
between rebel and government violence on the ground. Cyber
attacks by pro-
Ukrainian forces see no increase after shocks in kinetic
government operations, and
a positive, but uncertain increase after shocks in kinetic rebel
operations (third row,
first and second columns).

There is even less evidence that cyber attacks drive kinetic
operations. The
impulse–response function (IRF) curve for pro-Ukrainian
government violence is,
in fact, negative after shocks to rebel cyber operations (top row,
two rightmost
columns). Although this negative response might otherwise
suggest that cyber
attacks compel a decline in violence—consistent with coercive
success—the esti-
mate is also highly uncertain. Following shocks to pro-
Ukrainian cyber activities,
meanwhile, the main change in rebel kinetic operations is a
short-term increase in
volatility (second row, third column). In sum, the data suggest
that cyber attacks may
make violence less predictable but do not systematically change
its intensity.
Perhaps most surprisingly, there is little or no apparent strategic
interaction
between “cyber-warriors” on each side of the conflict. A shock
in pro-Ukrainian
cyber attacks yields no discernible change in pro-rebel cyber
attacks (bottom row,

third column) and vice versa (third row, fourth column). The
two cyber campaigns,
the data suggest, have unfolded independently of each other and
independently of
events on the ground.
As the diagonal elements in Figure 2 suggest, there is strong
autocorrelation in
each series. For each of the four categories, past shocks in
operations yield a sig-
nificant spike in subsequent operations. To evaluate whether the
other categories of
events can help us predict future values of each series, after we
take this autocorre-
lation into account, Table 3 reports the results of Granger
causality tests. The tests
confirm that past levels of prorebel and pro-Kyiv kinetic
operations help predict
each other’s future values. Kinetic operations, however, do not
appear to “Granger
cause”—or be “Granger caused” by—cyber attacks on either
side.
Table 4 reports the forecasting error variance decomposition,
representing the
proportion of variation in each series (rows) due to shocks in

each endogenous
variable (columns). For most variables, their own time-series
account for almost
all variation at the outset, but this dependency gradually
decreases. As before,
there is far more dependence within kinetic operations than
between kinetic and
cyber or within cyber actions. By the thirty-day point in the
daily time series,
shocks in rebel attacks account for 7 percent of variation in
Ukrainian government
operations, while shocks in government operations explain 12
percent of variation
in rebel violence.
Table 4. Variance Decomposition, Daily Time Series (Ukraine).
Operation type Kinetic (U) Kinetic (R) Cyber (U) Cyber (R)
Kinetic (U)
1 Day 1.000 .000 .000 .000
2 Days 0.920 .060 .002 .018
7 Days 0.906 .071 .002 .020
30 Days 0.906 .071 .002 .020

Kinetic (R)
1 Day 0.108 .892 .000 .000
2 Days 0.121 .873 .000 .006
7 Days 0.122 .870 .000 .008
30 Days 0.122 .870 .000 .008
Cyber (U)
1 Day 0.000 .002 .998 .000
2 Days 0.000 .002 .997 .000
7 Days 0.000 .003 .997 .000
30 Days 0.000 .003 .997 .000
Cyber (R)
1 Day 0.012 .023 .000 .964
2 Days 0.014 .023 .001 .962
7 Days 0.015 .023 .001 .961
30 Days 0.015 .023 .001 .961
Note: “U” indicates kinetic and cyber operations by pro-
Ukrainian government forces, and “R” indicates
operations by pro-Russian rebel forces.
Table 3. Granger Causality Test, Daily Time Series (Ukraine).
Effects F statistic p value
Kinetic (R) ! Kinetic (U) 40.26 .00
Cyber (U) ! Kinetic (U) 0.50 .48
Cyber (R) ! Kinetic (U) 0.09 .76
Kinetic (U) ! Kinetic (R) 12.29 .00
Cyber (U) ! Kinetic (R) 1.44 .23
Cyber (R) ! Kinetic (R) 2.70 .10
Kinetic (U) ! Cyber (U) 1.40 .24
Kinetic (R) ! Cyber (U) 1.88 .17
Cyber (R) ! Cyber (U) 0.00 .95
Kinetic (U) ! Cyber (R) 1.74 .19

Kinetic (R) ! Cyber (R) 0.14 .71
Cyber (U) ! Cyber (R) 0.89 .35
Note: “U” indicates reported kinetic and cyber operations by
Pro-Ukrainian government forces, and “R”
indicates operations by Pro-Russian rebel forces.
By contrast, shocks to cyber activities account for very little
variation in kinetic
operations. The highest value is for pro-Russian rebel cyber
activities, which
account for 2 percent of short-term variation in government
violence. Cyber attacks
by each side also have a relatively small impact on each other.
Indeed, rebel kinetic
operations explain more of the variation in cyber attacks by
each actor than do cyber
attacks by the other side.
In sum, our analysis suggests that low-level cyber attacks in
Ukraine have had no
effect on the timing of physical violence. Not only is there no
evidence that cyber
attacks have compelled opponents to de-escalate fighting, there
is no discernible

reciprocity between the cyber actors themselves. Each group of
hackers seems to
operate in its own bubble, disengaged from unfolding events in
both cyberspace and
the physical world.
Robustness Checks
To gauge the sensitivity of our results to various modeling and
measurement
choices, we conducted extensive robustness checks. We
summarize their results
briefly here (Table 5) and more fully in the Online Appendix.
The first set of tests considers vector autoregression models
with alternative
orderings of the four endogenous variables, which affects
estimation of impulse
responses. We find no substantive differences across the
twenty-four permutations.
In a second set of robustness checks, we account for systematic
differences in the
kinds of conflict events that Ukrainian and Russian media
report, which may bias
statistical estimates—for example, by underreporting violence
by a given actor.

Using kinetic data from exclusively Russian or exclusively
Ukrainian sources does
not change the results.
A third set of robustness tests examines different subsets of
cyber attacks.
Because purely disruptive activities may impose greater
immediate costs than
quasi-propagandistic hybrid attacks, pooling these events may
dilute their coercive
effect. Our results are consistent for all three subsets.
The last set of robustness checks examines different time
periods of the conflict,
since some cyber attacks predated military activity. In
particular, we compare the
period of intense fighting previously analyzed (May 11, 2014–
February 15, 2015) to
the entire date range for which we have data (February 28,
2014–February 29, 2016).
Our results remain unchanged.
Evidence from Interviews
In interviews, Russian and Ukrainian cyber security experts
highlighted five poten-

tial explanations for the apparent failure of cyber coercion in
Ukraine: (1) lack of
resources, (2) lack of coordination, (3) lack of targets, (4) lack
of audience, and (5)
lack of effort.
T
a
b
le
5
.
R
o
b
u
st
n
e
ss
C
h
e
ck
s

(U
k
ra
in
e
an
d
S
yr
ia
). U
k
ra
in
e
(m
ai
n
re
su
lt
s;
M
ay

1
1
,
2
0
1
4
–
F
e
b
ru
ar
y
1
5
,
2
0
1
5
)
ID
C
yb
e
r
IR
F
(d

)
IR
F
(w
)
IR
F
(o
)(
d
)
IR
F
(o
)(
w
)
IR
F
(d
)
so
u
rc
e

s
(R
U
)
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F
(d
)
so
u
rc
e
s
(U
)
1
D
is
ru
p
ti
o
n
an
d

b
o
th
K
in
(R
)
$
K
in
(U
)
K
in
(U
)
!
K
in
(R
)
K
in
(R
)

$
K
in
(U
)
K
in
(U
)
$
K
in
(R
)
K
in
(U
)
$
K
in
(R
)
K
in

(U
)
!
K
in
(G
)
ID
C
yb
e
r
G
C
T
(w
)
V
D
(d
)
(3
0
d
ay

)
V
D
(w
)
(1
2
w
e
e
k
)
1
D
is
ru
p
ti
o
n
an
d
b
o
th

K
in
(R
)
!
7
%
K
in
(U
)
K
in
(R
)
!
2
%
C
yb
(R
)
K
in
(U

)
!
1
2
%
K
in
(R
)
K
in
(U
)
!
2
%
C
yb
(R
)
K
in
(R
)
!

1
0
%
C
yb
(R
)
K
in
(U
)
!
2
1
%
K
in
(R
)
K
in
(U
)
!
1
7

%
C
yb
(R
)
C
yb
(U
)
!
4
%
C
yb
(R
)
ID
C
yb
e
r
IR
F
(d
)

IR
F
(w
)
G
C
T
(d
)
G
C
T
(w
)
V
D
(d
)
(3
0
d
ay
)
V

D
(w
)
(1
2
w
e
e
k
)
U
k
ra
in
e
(M
ar
ch
2
2
,
2
0
1
4
–
F
e
b

ru
ar
y
2
9
,
1
6
)
2
A
ll
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!

K
in
(R
)
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!
K
in
(R
)
K
in

(R
)
$
K
in
(U
)
C
yb
(U
/R
)
$
K
in
(U
)
C
yb
(U
)
$
K
in
(R

)
K
in
(R
)
!
K
in
/C
yb
(U
)
K
in
(U
)
!
C
yb
(R
)
K
in
(R
)

!
3
%
K
in
(U
)
K
in
(U
)
!
1
7
%
K
in
(R
)
K
in
(R
)
!
8

%
K
in
(U
)
K
in
(U
)
!
4
5
%
K
in
(R
)
K
in
(U
)
!
3
%
C

yb
(R
)
3
P
ro
p
ag
an
d
a
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!

K
in
(R
)
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!
K
in
(R
)
C
yb
(U

)
!
K
in
(U
)
K
in
(R
)$
K
in
(U
)
C
yb
(U
)
!
K
in
/C
yb
(R
)

K
in
(U
)
!
C
yb
(R
)
K
in
(R
)
!
K
in
(U
)/
C
yb
(R
)
K
in

(U
)
!
C
yb
(R
)
K
in
(R
)
!
3
%
K
in
(U
)
K
in
(U
)
!
1
8
%

K
in
(R
)
K
in
(R
)
!
9
%
K
in
(U
)
K
in
(R
)
!
3
%
C
yb

(R
)
K
in
(U
)
!
4
6
%
K
in
(R
)
K
in
(U
)
!
5
%
C
yb
(U
)

K
in
(U
)
!
2
%
C
yb
(R
)
4
D
is
ru
p
ti
o
n
K
in
(R
)
!
K
in

(U
)
K
in
(U
)
!
K
in
(R
)
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!

K
in
(R
)
K
in
(R
)
$
K
in
(U
)
C
yb
(U
)
$
K
in
(U
/R
)
K

in
(R
)
!
K
in
/C
yb
(U
)
K
in
(R
)
!
3
%
K
in
(U
)
K
in
(U
)

!
1
7
%
K
in
(R
)
K
in
(R
)
!
8
%
K
in
(U
)
K
in
(U
)
!
4

5
%
K
in
(R
)
5
D
is
ru
p
ti
o
n
an
d
b
o
th
K
in
(R
)
!
K
in

K
in
(R
)
K
in
(R
)
$
K
in
(U
)
C
yb
(U
)
$
K
in
(R
)
K
in

(U
)
!
C
yb
(U
)
K
in
(R
)
!
K
in
/C
yb
(U
)
K
in
(U
)
!
C
yb

(R
)
K
in
(R
)
!
3
%
K
in
(U
)
K
in
(U
)
!
1
7
%
K
in
(R
)

K
in
(R
)
!
8
%
K
in
(U
)
K
in
(U
)
!
4
5
%
K
in
(R
)
(c
o
n

ti
n
u
ed
)
334
T
a
b
le
5
.
(c
o
n
ti
n
u
e
d
)
ID
C
yb
e
r

IR
F
(d
)
IR
F
(w
)
IR
F
(o
)(
d
)
IR
F
(o
)(
w
)
IR
F
(d
)
so

u
rc
e
s
(R
U
)
IR
F
(d
)
so
u
rc
e
s
(U
)
U
k
ra
in
e
(M
ay

1
1
,
2
0
1
4
–
F
e
b
ru
ar
y
1
1
,
2
0
1
5
)
6
A
ll
K
in
(R
)

!
K
in
(U
)
K
in
(U
)
!
K
in
(R
)
K
in
(U
)
!
K
in
(R
)
K
in

(R
)
$
K
in
(U
)
K
in
(R
)
!
7
%
K
in
(U
)
K
in
(U
)
!
1
3

%
K
in
(R
)
K
in
(R
)
!
2
%
C
yb
(U
)
K
in
(R
)
!
1
8
%
C

yb
(R
)
K
in
(U
)
!
3
0
%
K
in
(R
)
K
in
(U
)
!
2
%
C
yb
(U

/R
)
C
yb
(U
)
!
4
%
K
in
(R
)
C
yb
(R
)
!
3
%
K
in
(U
)

7
P
ro
p
ag
an
d
a
K
in
(R
)
!
K
in
(U
)
K
in
(U
)
!
K
in
(R
)

C
yb
(R
)
!
K
in
(U
)
K
in
(U
)
!
K
in
(R
)
K
in
(R
)
$
K
in

(U
)
K
in
(R
)
!
C
yb
(R
)
K
in
(R
)
!
7
%
K
in
(U
)
K
in

(U
)
!
1
3
%
K
in
(R
)
K
in
(U
)
!
2
7
%
K
in
(R
)
K
in
(U
)

!
4
%
C
yb
(U
)
K
in
(R
)
!
5
%
C
yb
(U
)
K
in
(R
)
!
3
%

C
yb
(R
)
C
yb
(U
)
!
9
%
K
in
(U
)
C
yb
(U
)
!
2
%
K
in

(R
)
C
yb
(U
)
!
2
0
%
C
yb
(R
)
C
yb
(R
)
!
5
%
K
in
(U
)

C
yb
(R
)
!
2
%
C
yb
(U
)
8
D
is
ru
p
ti
o
n
K
in
(R
)
!
K
in

(U
)
K
in
(U
)
!
K
in
(R
)
K
in
(U
)
!
K
in
(R
)
K
in
(R
)
$

K
in
(U
)
K
in
(U
)
!
C
yb
(U
)
K
in
(R
)
!
7
%
K
in
(U
)

K
in
(R
)
!
2
%
C
yb
(R
)
K
in
(U
)
!
1
2
%
K
in
(R
)
C
yb

(R
)
!
2
%
K
in
(U
)
K
in
(U
)
!
2
9
%
K
in
(R
)
K
in
(U
)
!

3
4
%
C
yb
(U
)
K
in
(U
)
!
2
2
%
C
yb
(R
)
K
in
(R
)
!
3

%
K
in
(U
)
K
in
(R
)
!
1
0
%
C
yb
(U
)
K
in
(R
)
!
1
3
%

C
yb
(R
)
C
yb
(U
)
!
2
%
K
in
(U
)
C
yb
(U
)
!
6
%
C
yb
(R

)
C
yb
(R
)
!
3
%
K
in
(R
)
C
yb
(R
)
!
7
%
C
yb
(U
)
S
yr

ia
(M
ar
ch
1
7
,
2
0
1
1
–
Ju
ly
1
0
,
2
0
1
6
)
9
D
is
ru
p
ti
o

n
an
d
b
o
th
K
in
(G
)
!
K
in
(R
)
K
in
(R
)
$
K
in
(U
)
K

in
(U
)
!
2
%
K
in
(R
)
N
o
te
:
IR
F
¼
im
p
u
ls
e
re
sp
o
n

se
fu
n
ct
io
n
s;
G
C
T
¼
G
ra
n
ge
r
ca
u
sa
lit
y
te
st
s;
V

D
¼
va
ri
an
ce
d
e
co
m
p
o
si
ti
o
n
;
d
¼
d
ai
ly
;
w
¼
w
e

e
k
ly
;
o
¼
al
te
rn
at
iv
e
o
rd
e
ri
n
gs
;
R
U
¼
R
u
ss

ia
n
;
U
¼
U
k
ra
in
ia
n
.
335
The first explanation for coercive failure emphasizes limited
resources and cap-
abilities, particularly for the Ukrainian government. Ten years
ago, the SBU briefly
had a cyber department but shut it down after a year (Kostyuk
and Zhukov 2017, #3).
This unit has recently reopened but continues to lack funding
and personnel (Kos-
tyuk and Zhukov 2017, #3, #9). It is possible that, with
adequate resources, cap-

abilities, and human capital, the Ukrainian cyber campaign
might have been more
effective. Resource constraints, however, do not explain
coercive failure on the pro-
Russian side, where investment in cyber capabilities is more
robust.
A second explanation is lack of government coordination with
hackers, especially
in Kyiv (Maurer and Geers 2015). UCF founder Eugene
Dokukin claims to regularly
provide the SBU with intelligence from hacked CCTV cameras
and has offered
cooperation in the past, with no success (Kostyuk and Zhukov
2017, #1). The SBU’s
lack of desire to cooperate with the UCF could be due to the
illegality of the latter’s
activities or the low priority the SBU assigns to cyber actions in
the first place
(Kostyuk and Zhukov 2017, #1, #3, #9). Yet again, this
explanation is less plausible
on the pro-Russian side, where the Kremlin has cultivated
extensive ties with non-
state hacktivists.
A third explanation is that—even with requisite capabilities and

coordination—
there are few opportune targets for disruption in Ukraine. Most
industrial control
systems that run Ukraine’s critical infrastructure—particularly
its Soviet-era com-
ponents—are off-line, making remote access difficult (Geers
2015; Kostyuk and
Zhukov 2017, #3, #13). Yet some experts disagreed, noting that
“weakness of
infrastructure [security] should have provoked a DDoS attack”
(Kostyuk and Zhu-
kov 2017, #11). The 2015 and 2016 hacks of Ukraine’s power
grid also seem to
challenge this explanation.
The peculiarities of Ukraine’s online population represent a
fourth explanation
for the indecisiveness of cyber attacks. Since only 44.1 percent
of Ukrainians have
Internet access—compared to 88.5 percent in the United States
and 71.3 percent in
Russia (see http://www.internetlivestats.com/internet-users-by-
country/)—and most
use it only for social media, a low-level cyber attack that blocks
or defaces govern-

ment websites is unlikely to influence the masses (Kostyuk and
Zhukov 2017, #3).
Some experts speculated that this online population pays more
attention to purely
propagandistic campaigns than disruptive ones (Kostyuk and
Zhukov 2017, #7,
#11). Our data suggest that, even if this were the case,
propagandistic attacks still
had no effect on violence.
The final explanation is that cyber compellence failed because it
was never seri-
ously attempted. At first, our interviews with individual hackers
revealed no shortage
of coercive intent. UCF leader Eugene Dokukin claimed to
conduct low-level attacks
daily and vowed to continue until pro-Russian rebels lay down
their arms. Dokukin
further insisted—contrary to our findings—that there is close
coordination between
Russia’s cyber and kinetic campaigns (Kostyuk and Zhukov
2017, #1).
While UCF and other nonstate groups have explicitly sought to
affect battlefield

outcomes, some interviewees questioned whether this intent
extended to the Russian
http://www.internetlivestats.com/internet-users-by-country/
government. Since Ukraine’s information and
telecommunication networks gener-
ally use Russian hardware and software, Moscow can monitor
its neighbor with
assets already in place (Kostyuk and Zhukov 2017, #5, #12).
17
This access, along
with vigorous cyber espionage—some of it ongoing since
2010—may create incen-
tives against more aggressive actions, which could compromise
valuable sources of
intelligence.
Consistent with the “lack of effort” explanation, some experts
noted a shift in
Russia’s broader cyber strategy, away from disruption and
toward propaganda (Kos-
tyuk and Zhukov 2017, #11). When in 2011 Vyacheslav Volodin
replaced Vladislav

Surkov as head of the Presidential Administration, he toughened
existing laws
against Russia’s opposition and promoted the use of mass media
and online plat-
forms—tools already mostly under state control—to conduct
information cam-
paigns. If Russia’s cyber activities have shifted toward
propaganda due to this
strategy change, weak short-term battlefield effects should not
be surprising (Kos-
tyuk and Zhukov 2017, #2, #14).
Evidence beyond Ukraine: Syria’s Digital Front
According to evidence from microlevel data and interviews,
cyber attacks did not
affect battlefield events in Ukraine. During one of the first
armed conflicts where
both sides used low-level cyber actions extensively, events in
the digital realm have
unfolded independently of—and have had no discernible effect
on—events on the
ground. Conditions in Ukraine were in many ways optimal to
observe the coercive
impact of cyber actions, for reasons we already discussed (i.e.,
visibility of major

attacks, regular claims of responsibility, less uncertainty over
attribution). Yet we
found no evidence that low-level cyber attacks affected physical
violence. Nor did
hackers on each side even affect each other’s activities.
While important, Ukraine is not the only contemporary conflict
with a significant
cyber dimension. In Syria, state and nonstate actors have
employed low-level cyber
actions extensively for propaganda and disruption,
complementing traditional tools
of warfare in the deadliest conflict ongoing today. Syria’s war
has also lasted three
years longer than Ukraine’s. Over this time, its digital front has
expanded in scope
and sophistication, offering a glimpse of cyber coercion in a
more protracted setting.
An in-depth study of Syria’s digital front lies beyond the scope
of this article. A
brief analysis of the data, however, suggests that our findings
from Ukraine may be
part of a broader pattern: cyber capabilities have not yet
evolved to the point of

having an impact on physical violence.
To evaluate the effectiveness of cyber compellence in this
second case, we
replicated the model in (equation 1), using an analogous daily
time series of cyber
attacks and violent events in Syria. Our data comprise 9,282
kinetic and 682 low-
level cyber attacks ranging from March 2011 until July 2016.
18
Table 2 provides a
breakdown of cyber techniques used in the Syrian conflict, their
brief description,
and frequency.
19
Our data on kinetic operations rely on human-assisted machine
coding of event reports from the International Institute for
Strategic Studies Armed
Conflict Database (see Online Appendix for details).
Given the complex nature of the Syrian conflict and the
multiple parties involved,

we restrict our analysis only to operations by progovernment
forces (i.e., Syrian
Army, Hezbollah and pro-Assad militias) and the main rebel
opposition (i.e., Free
Syrian Army, Jaish al-Fatah, including Al Nusra Front). Table 1
provides a list of
cyber actors in the Syrian conflict, their targets, and frequency
of their activities.
The dynamics of cyber and kinetic operations in Syria exhibit
similar patterns to
what we saw in Ukraine. Raw data (Figure 3a and b) suggest
relatively little overlap
in timing, especially at the beginning of the conflict. The IRF
curves in Figure 4
show a rise in rebel operations following shocks to government
operations (second
row, first column), and mostly negligible (though negative)
links between cyber and
kinetic operations, and across cyber attacks by each actor. Links
between kinetic
Figure 3. Cyber and kinetic operations in Syria (March 2011–
July 2016). G (blue) indicates
operations by pro-Assad government forces; R (red) indicates
operations by anti-Assad rebel
groups.

R
es
po
ns
e:
K
in
et
ic
(G
)
K
in
et
ic
(R
)
C
yb
er
(G
)
C
yb
er

(R
)
Im
pu
ls
e:
K
in
et
ic
(G
)
K
in
et
ic
(R
)
C
yb
er
(G
)
C
yb
er
(R
)
0

5
10
15
20
25
30
0.01.02.03.0
0
5
10
15
20
25
30
−0.20−0.100.00
0
5
10
15
20
25
30

−0.100.000.10
0
5
10
15
20
25
30
−0.20−0.100.00
0
5
10
15
20
25
30
−0.020.020.06
0
5
10
15
20
25

30
0.00.10.20.3
0
5
10
15
20
25
30
−0.025−0.0100.000
0
5
10
15
20
25
30
−0.025−0.0100.005
0
5
10
15

20
25
30
−0.0100.000
0
5
10
15
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25
30
−0.020−0.0100.000
0
5
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20
25
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0.000.100.20
0
5

10
15
20
25
30
−0.020−0.0100.000
0
5
10
15
20
25
30
−0.0100.000
0
5
10
15
20
25
30
−0.0100.000

0
5
10
15
20
25
30
−0.0100.000
0
5
10
15
20
25
30
0.000.050.100.15
F
ig
u
r
e
4
.

Im
p
u
ls
e
re
sp
o
n
se
m
at
ri
x
,
d
ai
ly
ti
m
e
se
ri
e
s
(S
yr

ia
).
L
ig
h
t
gr
ay
ar
e
a
re
p
re
se
n
ts
9
5
p
e
rc
e
n
t
co

n
fi
d
e
n
ce
in
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operations—and their disconnect from cyber attacks—are also
evident in variance
decomposition results, and Granger tests, provided in the Online
Appendix.
There are several reasons for caution in interpreting these
results. The Syrian

conflict involves a larger constellation of actors than Ukraine,
and our dyadic anal-
ysis may overlook significant interactions elsewhere,
particularly between actors
with more developed cyber capabilities (e.g., Russia, United
States). We also lack
interview evidence that might help contextualize the null effect.
However tentative,
these results do align with what we saw in Ukraine: low-level
cyber attacks have had
little or no impact on violence.
Conclusion
The evidence we presented in this article—based on analysis of
new data and expert
interviews—suggests that cyber attacks are ineffective as a tool
of coercion in war.
Although kinetic operations explain the timing of other kinetic
operations, low-level
cyber attacks have no discernible effect on violence in the
physical world. In
Ukraine and Syria, the “cyberwar” has unfolded in isolation
from the rest of the
conflict.

This finding has several implications for theory and policy.
First, by providing the
first statistical analysis of modern low-level cyber campaigns,
our study comple-
ments the qualitative focus of previous empirical work. Second,
our research sheds
light on a theoretical question about the strength and direction
of the cyber–kinetic
relationship and—in so doing—begins to fill an empirical gap in
political science
literature on this topic. Third, to the extent that policymakers
might overestimate the
importance of cyber actions due to a lack of empirical evidence
to the contrary, our
findings can potentially help correct this misperception. Finally,
and more worry-
ingly, our results suggest that—due to their disconnect from
physical violence—
low-level cyber attacks are very difficult to predict.
Further research is needed to understand the dynamics of low-
level cyber attacks.
One such area of research is cyber coercion in the context of
symmetric, conventional
war. While our study helps illuminate dynamics of cyber

compellence between parties
with asymmetric capabilities, we may well observe different
patterns when major
powers use cyberspace against peer competitors. Thankfully, no
armed conflict has
yet provided researchers with the data needed to evaluate this
possibility.
Second, our scope in this article has been exclusively on short-
term military
consequences rather than long-term political effects. The latter
are no less theore-
tically significant, but—unlike simple counts of violent
events—potentially more
difficult to measure and analyze. A study of long-term political
effects would also
need to more systematically incorporate purely propagandistic
cyber activities and
their impact on public opinion, which we omitted here due to
our focus on short-term
military compellence.
Although the secretive nature of many ongoing physical and
digital operations is
a challenge for this research, questions over the coercive
potential of cyber attacks

will become only more salient in the future. In June 2017, the
New York Times
reported that US cyber efforts against the IS—previously lauded
as “a [major] shift
in America’s war-fighting strategy and power projection”
(Sabah 2016)—have
yielded few tangible successes (Sanger and Schmitt 2017). Our
data from Ukraine
indicate that the US experience may be part of a broader
pattern.
At best, coordination between low-level cyber and kinetic
operations today is on
roughly the same level as that between airpower and ground
operations in World
War I. Back then, armies were increasingly using aircraft for
reconnaissance and
surveillance on the front but were not yet able to fully exploit
their potential for
ground combat support and strategic bombing. That revolution
appeared on the
battlefield twenty-five years later, with devastating effect. As

cyber capabilities
develop and synchronization challenges become less severe,
there will be a growing
need for assessments of how far we have come. We hope that
analyses of the sort we
provided in these pages can serve as an early benchmark.
Authors’ Note
A previous version of this article was presented at the 2015
Peace Science Society Interna-
tional annual meeting, Oxford, MS, and at the Association for
the Study of Nationalities
Convention at New York, NY.
Acknowledgments
We are grateful to Maura Drabik, Paulina Knoblock, Neil
Schwartz, and Alyssa Wallace for
excellent research assistance. Robert Axelrod, Myriam Dunn-
Cavelty, Eric Gartzke, Miguel
Gomez, Todd Lehmann, Jon Lindsay, Tim Maurer, Brandon
Valeriano, Christopher Whyte,
and workshop participants of the Conflict & Peace, Research &
Development workshop at the
University of Michigan, of the Bridging the Gap Workshop on
Cyber Conflict at Columbia

University, of the Cross-Domain Deterrence lab at the
University of California, San Diego,
and of the Center for Security Studies at ETH Zurich provided
helpful comments on the earlier
drafts of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, author-
ship, and/or publication of this article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of
this article.
Supplemental Material
Supplemental material for this article is available online.
Notes
1. We define coercion as an attempt to influence a target’s
behavior by increasing the costs
associated with an unwanted action. Cyber activities apply these
costs through the

disruption, destruction, malicious control, or surveillance of a
computing environment or
infrastructure (Kissel 2013). Kinetic or physical operations
apply costs through physical
force. Low-level cyber attacks cause minor disruptions and
include web-page deface-
ments, phishing, distributed denial of service attacks. High-
level cyber attacks include
serious disruption with loss of life and extensive infrastructure
disruption.
2. Deterrence seeks to convince a target to not start an
unwanted action. Compellence seeks
to convince the target to stop an ongoing unwanted action.
3. We use propaganda when referring to the propaganda
category, cyber attacks when
referring to disruption (Cartwright and James 2010), and hybrid
cyber operations when
referring to hybrids of the two.
4. For example, US Cyber Command has used low-level cyber
operations to “disrupt the
ability of the Islamic State to spread its message, attract new
adherents, circulate orders

from commanders and [pay] its fighters” (Sanger 2016).
5. A zero-day vulnerability is a security hole previously
unknown to the target.
6. This trade-off is not unique to the cyber domain. In civil
conflict, for example, pro-
government militias pose a similar dilemma for state repression
(Gohdes and Carey
2017).
7. Another potentially illuminating case, which we are unable to
analyze here, is the Rus-
sian–Georgian War of 2008. This earlier conflict laid much of
the groundwork for the
crisis in Ukraine. For the first time in history, cyberspace
played a highly visible role in
armed conflict, facilitating strategic communication between
civilian and military lead-
ership, disabling or degrading key infrastructure, exploiting or
hijacking government
computer systems, while also serving as a tool for propaganda
(Deibert, Rohozinski, and
Crete-Nishihata 2012). While some of the lessons of the
Russian–Georgian War might
well run counter to our claims in this article, its short duration

(five days) complicates
analysis, for three reasons. First is a lack of sufficient variation
in cyber attacks over this
abbreviated period. Second is the difficulty of differentiating
the “cyber effect” from the
near-simultaneous effects of conventional military operations.
Third is the problem of
generalizability: its five-day duration is an extreme outlier
among interstate and civil
wars (interstate wars, on average, tend to last a few years; the
average civil war lasts
between seven and twelve years post-1945). For these reasons,
we are unable to quanti-
tatively establish whether synchronized usage of cyberspace,
along with traditional tools
of war, had a tangible coercive impact in Georgia.
8. Sections 3.1 and 3.2 along with the Online Appendix provide
an overview of these
sources.
9. Rebel sources include Donetsk News Agency. Russian
sources include RIA Novosti,
Sputnik, and Vesti.ru. Ukrainian sources include Interfax-
Ukraine, Segodnya, and

RBK-Ukraina. Western sources include technical (Arstechnica,
Digital Dao, Information
Week, F-Secure, Graham Cluley, and TechWeek Europe) and
mainstream news (Die
Welt, Newsweek, New York Times, Politico, Postimees
(Estonia), Security Affairs, and
The Christian Science Monitor).
10. Our Ukrainian interviewees included experts from the
Ukrainian Cyber Forces, Computer
Emergency Response Team of Ukraine, StopFake, InfoPulse,
Luxoft, Berezha Security,
Open Ukraine Foundation, and the Ukrainian Central Election
Committee. Western
experts’ affiliations include New York University, Chatham
House, the Center for Stra-
tegic and International Studies, RAND Corporation, The
Economist, Mashable, New
America Foundation, and the North Atlantic Treaty
Organization Cyber Center of Excel-
lence. Due to the complicated political situation in Russia at the
time, many of our

contacts there refused to speak on record, with the exception of
a journalist from Agen-
tura.ru. However, many Western interviewees have lived in
Russia, speak the language,
and are knowledgeable on Russia’s information security issues.
11. This is a very conservative standard of attribution, since it
includes only direct claims of
responsibility and not accusations by others—even if the latter
are substantiated by
evidence. For instance, we marked as “disputed” the cyber
espionage operation Arma-
geddon—which multiple governments and private security firms
have attributed to the
Russian state—because Moscow never claimed responsibility.
12. Excluded operations included the malware Blackenergy,
first launched by Quedagh in
2010; Operation Potao Express, a targeted espionage campaign
launched in 2011 against
the Ukrainian government, military, and news agencies; and
Snake, a cyber espionage
campaign against Ukrainian computer systems.
13. We aggregated these data to daily time series because
geolocation is not possible.

Although some individual cyber attacks could, in theory, be
tracked to their targets, they
represent a small proportion of events. As a result, our cyber
data are national-level time
series. Even if we could geolocate all targets of cyber attacks,
the diffuse nature of the
target set makes spatial matching difficult—servers do not need
to be physically located
in the war zone for service disruptions to have an effect in the
war zone.
14. Ukrainian sources include Channel 5, Espresso.tv,
Information Resistance, 112 Ukraina,
and the newswire services Interfax-Ukraine and Ukrinform.
Russian sources include the
state-owned television news channel Russia-24; the independent
TV station Dozhd;
nongovernment news websites Gazeta.ru, Lenta.ru, and BFM.ru;
and the Interfax news-
wire service. Pro-rebel sources include Donetsk News Agency,
NewsFront, and Rus-
vesna.su. Also included are the Russian language edition of
Wikipedia and daily
briefings from the Organization for Security and Co-operation
in Europe Special Mon-

itoring Mission to Ukraine. Since these are mostly online
resources, cyber disruptions can
potentially cause underreporting of violence. Our approach
helps ensure that if, for
instance, a Ukrainian media firms’ servers went down,
information could still reach the
outside world through one of the sixteen other sources. While
unlikely, such endogenous
disruptions should increase our chances of finding a coercive
cyber effect.
15. Because geolocation is not possible for cyber attacks, we
aggregate the physical violence
data to daily time series to merge and analyze the data sets.
16. Vector autoregression is a common method to study
interdependence among multiple
time series in economics and political science. Previous
applications to conflict research
include studies of reciprocity in civil conflicts (Pevehouse and
Goldstein 1999) and the
dynamics of terrorism (Enders and Sandler 2000; Bejan and
Parkin 2015).
17. An example is Russia’s Sistema operativno-rozysknykh
meropriyatiy (system for opera-
tional investigative activities), which searches and monitors

electronic communications.
18. Sources of cyber operations include social media accounts
of anonymous or anonymous-
supported groups (e.g., New World Hacking); Syrian Electronic
Army’s social media
accounts; reports by tech companies (e.g., risk-based security,
Electronic Frontier Foun-
dation); computer-security news sources including Graham
Cluley, TechWeek Europe,
Arstechnica, Information Week, Digital Dao, Computer Weekly,
Tech News, Wired, and
Security Affairs; Middle Eastern mass media sources (e.g.,
Turkish News, Arabiya, Doha
News); Russian mass media and social media (e.g., RT.com,
Yahoo.com); and Western
news sources (e.g., Security Affairs, The Christian Science
Monitor, Politico, Die Welt,
Reuters, International Business Times, Mashable, Washington
Times, The Guardian,
British Broadcasting Corporation, etc.).
19. Since propaganda operations are not a major focus of our

article, we collected only a
small sample of such events during the Syrian conflict.
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ISSLJC-SETUP-ALIEN-PAREU-BEGUN-LIKES
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Your new textbook provides 12-month access to digital
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code, web chapters, quizzes, and
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resources.

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Website for Daniel Liang’s
Introduction to Java™ Programming and Data Structures,
Comprehensive Version,
Eleventh Edition, Global Edition.
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IntroductIon to
Java
ProgrammIng and
data StructureS
comPrehenSIve verSIon
Eleventh Edition
Global Edition
Y. daniel Liang
Armstrong State University
™
330 Hudson Street, NY NY 10013
A01_LIAN1878_11_GE_FM.indd 1 1/2/18 11:57 PM
To Samantha, Michael, and Michelle
Java™ and Netbeans™ screenshots ©2017 by Oracle
Corporation, all rights reserved. Reprinted with permission.
Credits and acknowledgments borrowed from other sources and
reproduced, with permission, in this textbook appear
on the appropriate page within text. Microsoft and/or its
respective suppliers make no representations about the suit-
ability of the information contained in the documents and

related graphics published as part of the services for any
purpose. All such documents and related graphics are provided
“as is” without warranty of any kind. Microsoft and/
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Pearson Education Limited
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and Associated Companies throughout the world
Visit us on the World Wide Web at:
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© Pearson Education Limited 2019

The rights of Y. Daniel Liang to be identified as the author of
this work have been asserted by him in accordance
with the Copyright, Designs and Patents Act 1988.
Authorized adaptation from the United States edition, entitled
Introduction to Java Programming and Data
Structures, Comprehensive Version, 11th Edition, ISBN 978-0-
13-467094-2 by Y. Daniel Liang, published by
Pearson Education © 2018.
All rights reserved. No part of this publication may be
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Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby
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owners. The use of any trademark in this text does
not vest in the author or publisher any trademark ownership
rights in such trademarks, nor does the use of such
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book by such owners.
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A catalogue record for this book is available from the British
Library
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Typeset by SPi Global ISBN-10: 1-292-22187-9
Printed and bound by Vivar in Malaysia ISBN-13: 978-1-292-
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3
Dear Reader,
Many of you have provided feedback on earlier editions of this
book, and your comments and
suggestions have greatly improved the book. This edition has
been substantially enhanced in
presentation, organization, examples, exercises, and
supplements.
The book is fundamentals first by introducing basic
programming concepts and techniques
before designing custom classes. The fundamental concepts and
techniques of selection
statements, loops, methods, and arrays are the foundation for
programming. Building this
strong foundation prepares students to learn object-oriented
programming and advanced Java
programming.
This book teaches programming in a problem-driven way that
focuses on problem solv-
ing rather than syntax. We make introductory programming
interesting by using thought-
provoking problems in a broad context. The central thread of

early chapters is on problem
solving. Appropriate syntax and library are introduced to enable
readers to write programs for
solving the problems. To support the teaching of programming
in a problem-driven way, the
book provides a wide variety of problems at various levels of
difficulty to motivate students.
To appeal to students in all majors, the problems cover many
application areas, including
math, science, business, financial, gaming, animation, and
multimedia.
The book seamlessly integrates programming, data structures,
and algorithms into one text.
It employs a practical approach to teach data structures. We
first introduce how to use various
data structures to develop efficient algorithms, and then show
how to implement these data
structures. Through implementation, students gain a deep
understanding on the efficiency of
data structures and on how and when to use certain data
structures. Finally, we design and
implement custom data structures for trees and graphs.
The book is widely used in the introductory programming, data
structures, and algorithms
courses in the universities around the world. This
comprehensive version covers fundamen-
tals of programming, object-oriented programming, GUI
programming, data structures, algo-
rithms, concurrency, networking, database, and Web
programming. It is designed to prepare
students to become proficient Java programmers. A brief
version (Introduction to Java Pro-
gramming, Brief Version, Eleventh Edition, Global Edition) is
available for a first course on

programming, commonly known as CS1. The brief version
contains the first 18 chapters of
the comprehensive version.
The best way to teach programming is by example, and the only
way to learn programming
is by doing. Basic concepts are explained by example and a
large number of exercises with
various levels of difficulty are provided for students to practice.
For our programming courses,
we assign programming exercises after each lecture.
Our goal is to produce a text that teaches problem solving and
programming in a broad
context using a wide variety of interesting examples. If you
have any comments on and
suggestions for improving the book, please email me.
Sincerely,
Y. Daniel Liang
[email protected]
www.pearsonglobaleditions.com/Liang
fundamentals-first
problem-driven
data structures
comprehensive version
brief version
Preface

4 Preface
ACM/IEEE Curricular 2013 and ABET
Course Assessment
The new ACM/IEEE Computer Science Curricular 2013 defines
the Body of Knowledge
organized into 18 Knowledge Areas. To help instructors design
the courses based on this
book, we provide sample syllabi to identify the Knowledge
Areas and Knowledge Units.
The sample syllabi are for a three semester course sequence
and serve as an example for
institutional customization. The sample syllabi are accessible
from the Instructor Resource
Center.
Many of our users are from the ABET-accredited programs. A
key component of the
ABET accreditation is to identify the weakness through
continuous course assessment
against the course outcomes. We provide sample course
outcomes for the courses and sam-
ple exams for measuring course outcomes on the Instructor
Resource Center.
What’s New in This Edition?
This edition is completely revised in every detail to enhance
clarity, presentation, content,
examples, and exercises. The major improvements are as
follows:
■■ The book’s title is changed to Introduction to Java

Programming and Data Structures with
new enhancements on data structures. The book uses a practical
approach to introduce
design, implement, and use data structures and covers all topics
in a typical data structures
course. Additionally, it provides bonus chapters that cover
advanced data structures such
as 2-4 trees, B-trees, and red-black trees.
■■ Updated to the latest Java technology. Examples and
exercises are improved and simplified
by using the new features in Java 8.
■■ The default and static methods are introduced for interfaces
in Chapter 13.
■■ The GUI chapters are updated to JavaFX 8. The examples
are revised. The user interfaces
in the examples and exercises are now resizable and displayed
in the center of the window.
■■ Inner classes, anonymous inner classes, and lambda
expressions are covered using practi-
cal examples in Chapter 15.
■■ More examples and exercises in the data structures chapters
use lambda expressions to
simplify coding. Method references are introduced along with
the Comparator interface
in Section 20.6.
■■ The forEach method is introduced in Chapter 20 as a simple
alternative to the foreach
loop for applying an action to each element in a collection.
■■ Use the default methods for interfaces in Java 8 to redesign

and simplify MyList,
MyArrayList, MyLinkedList, Tree, BST, AVLTree, MyMap,
MyHashMap, MySet,
MyHashSet, Graph, UnweightedGraph, and WeightedGraph in
Chapters 24–29.
■■ Chapter 30 is brand new to introduce aggregate operations
for collection streams.
■■ FXML and the Scene Builder visual tool are introduced in
Chapter 31.
■■ The Companion Website has been redesigned with new
interactive quiz, CheckPoint ques-
tions, animations, and live coding.
■■ More than 200 additional programming exercises with
solutions are provided to the
instructor on the Instructor Resource Center. These exercises
are not printed in the text.
Preface 5
Pedagogical Features
The book uses the following elements to help students get the
most from the material:
■■ The Objectives at the beginning of each chapter list what
students should learn from
the chapter. This will help them determine whether they have
met the objectives after
completing the chapter.

■■ The Introduction opens the discussion with a thought-
provoking question to motivate the
reader to delve into the chapter.
■■ Key Points highlight the important concepts covered in each
section.
■■ Check Points provide review questions to help students track
their progress as they read
through the chapter and evaluate their learning.
■■ Problems and Case Studies, carefully chosen and presented
in an easy-to-follow style,
teach problem solving and programming concepts. The book
uses many small, simple, and
stimulating examples to demonstrate important ideas.
■■ The Chapter Summary reviews the important subjects that
students should understand
and remember. It helps them reinforce the key concepts they
have learned in the chapter.
■■ Quizzes are accessible online, grouped by sections, for
students to do self-test on
programming concepts and techniques.
■■ Programming Exercises are grouped by sections to provide
students with opportunities to
apply the new skills they have learned on their own. The level
of difficulty is rated as easy
(no asterisk), moderate (*), hard (**), or challenging (***). The
trick of learning program-
ming is practice, practice, and practice. To that end, the book
provides a great many exer-
cises. Additionally, more than 200 programming exercises with

solutions are provided to
the instructors on the Instructor Resource Center. These
exercises are not printed in the text.
■■ Notes, Tips, Cautions, and Design Guides are inserted
throughout the text to offer
valuable advice and insight on important aspects of program
development.
Note
Provides additional information on the subject and reinforces
important concepts.
Tip
Teaches good programming style and practice.
Caution
Helps students steer away from the pitfalls of programming
errors.
Design Guide
Provides guidelines for designing programs.
Flexible Chapter Orderings
The book is designed to provide flexible chapter orderings to
enable GUI, exception handling,
recursion, generics, and the Java Collections Framework to be
covered earlier or later.
The diagram on the next page shows the chapter dependencies.
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6 Preface
Organization of the Book
The chapters can be grouped into five parts that, taken together,
form a comprehensive introduc-
tion to Java programming, data structures and algorithms, and
database and Web programming.
Because knowledge is cumulative, the early chapters provide
the conceptual basis for under-
standing programming and guide students through simple
examples and exercises; subsequent
chapters progressively present Java programming in detail,
culminating with the development
of comprehensive Java applications. The appendixes contain a
mixed bag of topics, including an
introduction to number systems, bitwise operations, regular
expressions, and enumerated types.
Part I: Fundamentals of Programming (Chapters 1–8)
The first part of the book is a stepping stone, preparing you to
embark on the journey of learning
Java. You will begin to learn about Java (Chapter 1) and
fundamental programming techniques
with primitive data types, variables, constants, assignments,
expressions, and operators ( Chapter 2),
selection statements (Chapter 3), mathematical functions,
characters, and strings (Chapter 4), loops
(Chapter 5), methods (Chapter 6), and arrays (Chapters 7–8).

After Chapter 7, you can jump to
Chapter 18 to learn how to write recursive methods for solving
inherently recursive problems.
Part II: Object-Oriented Programming (Chapters 9–13, and 17)
This part introduces object-oriented programming. Java is an
object-oriented programming
language that uses abstraction, encapsulation, inheritance, and
polymorphism to provide
great flexibility, modularity, and reusability in developing
software. You will learn program-
ming with objects and classes (Chapters 9–10), class inheritance
(Chapter 11), polymorphism
( Chapter 11), exception handling (Chapter 12), abstract classes
(Chapter 13), and interfaces
(Chapter 13). Text I/O is introduced in Chapter 12 and binary
I/O is discussed in Chapter 17.
Part III: GUI Programming (Chapters 14–16 and Bonus Chapter
31)
JavaFX is a new framework for developing Java GUI programs.
It is not only useful for
developing GUI programs, but also an excellent pedagogical
tool for learning object-oriented
programming. This part introduces Java GUI programming
using JavaFX in Chapters 14–16.
Major topics include GUI basics (Chapter 14), container panes
(Chapter 14), drawing shapes
(Chapter 14), event-driven programming (Chapter 15),
animations (Chapter 15), and GUI
controls (Chapter 16), and playing audio and video (Chapter
16). You will learn the architecture
of JavaFX GUI programming and use the controls, shapes,
panes, image, and video to develop

useful applications. Chapter 31 covers advanced features in
JavaFX.
Part IV: Data Structures and Algorithms (Chapters 18–30 and
Bonus Chapters 42–43)
This part covers the main subjects in a typical data structures
and algorithms course. Chapter 18
introduces recursion to write methods for solving inherently
recursive problems. Chapter 19 presents
how generics can improve software reliability. Chapters 20 and
21 introduce the Java Collection
Framework, which defines a set of useful API for data
structures. Chapter 22 discusses measur-
ing algorithm efficiency in order to choose an appropriate
algorithm for applications. Chapter 23
describes classic sorting algorithms. You will learn how to
implement several classic data struc-
tures lists, queues, and priority queues in Chapter 24. Chapters
25 and 26 introduce binary search
trees and AVL trees. Chapter 27 presents hashing and
implementing maps and sets using hashing.
Chapters 28 and 29 introduce graph applications. Chapter 30
introduces aggregate operations for
collection streams. The 2-4 trees, B-trees, and red-black trees
are covered in Bonus Chapters 42–43.
Part V: Advanced Java Programming (Chapters 32-41, 44)
This part of the book is devoted to advanced Java programming.
Chapter 32 treats the use of
multithreading to make programs more responsive and
interactive and introduces parallel pro-
gramming. Chapter 33 discusses how to write programs that talk
with each other from different

Preface 7
hosts over the Internet. Chapter 34 introduces the use of Java to
develop database projects.
Chapter 35 delves into advanced Java database programming.
Chapter 36 covers the use of
internationalization support to develop projects for international
audiences. Chapters 37 and
38 introduce how to use Java servlets and JavaServer Pages to
generate dynamic content from
Web servers. Chapter 39 introduces modern Web application
development using JavaServer
Faces. Chapter 40 introduces remote method invocation and
Chapter 41 discusses Web ser-
vices. Chapter 44 introduces testing Java programs using JUnit.
Appendixes
This part of the book covers a mixed bag of topics. Appendix A
lists Java keywords. Appendix B
gives tables of ASCII characters and their associated codes in
decimal and in hex. Appen-
dix C shows the operator precedence. Appendix D summarizes
Java modifiers and their usage.
Appendix E discusses special floating-point values. Appendix F
introduces number systems and
conversions among binary, decimal, and hex numbers. Finally,
Appendix G introduces bitwise
operations. Appendix H introduces regular expressions.
Appendix I covers enumerated types.
Java Development Tools

You can use a text editor, such as the Windows Notepad or
WordPad, to create Java programs
and to compile and run the programs from the command
window. You can also use a Java
development tool, such as NetBeans or Eclipse. These tools
support an integrated develop-
ment environment (IDE) for developing Java programs quickly.
Editing, compiling, building,
executing, and debugging programs are integrated in one
graphical user interface. Using these
tools effectively can greatly increase your programming
productivity. NetBeans and Eclipse
are easy to use if you follow the tutorials. Tutorials on
NetBeans and Eclipse can be found in
the supplements on the Companion Website
www.pearsonglobaleditions.com/Liang.
Student Resources
The Companion Website
(www.pearsonglobaleditions.com/Liang) contains the following
resources:
■■ Answers to CheckPoint questions
■■
Solution
s to majority of even-numbered programming exercises
■■ Source code for the examples in the book

■■ Interactive quiz (organized by sections for each chapter)
■■ Supplements
■■ Debugging tips
■■ Video notes
■■ Algorithm animations
Supplements
The text covers the essential subjects. The supplements extend
the text to introduce additional
topics that might be of interest to readers. The supplements are
available from the Companion
Website.
IDE tutorials
8 Preface

Instructor Resources
The Companion Website, accessible from
www.pearsonglobaleditions.com/Liang, contains the
following resources:
■■ Microsoft PowerPoint slides with interactive buttons to view
full-color, syntax-highlighted
source code and to run programs without leaving the slides.
■■

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