Secure intrusion detection and attack measure selection

Advances in Networks
2013; 1(2): 26-33
Published online June 10, 2013 (http://www.sciencepublishinggroup.com/j/net)
doi: 10.11648/j.net.20130102.12
Secure intrusion detection and attack measure selection
in virtual network systems
S. Uvaraj 1
, S. Suresh2
, N. Kannaiya Raja3
1
Arulmigu Meenakshi Amman College of Engineering, Kanchipuram
2
Sri Venkateswara College of Engineering, Kanchipuram
3
Defence Engineering College, Ethiopia
Email address:
ujrj@rediffmail.com(S. Uvaraj), ss12oct92@gmail.com(S. Suresh), kanniya13@hotmail.co.in(N. KannaiyaRaja)
To cite this article:
S. Uvaraj, S. Suresh, N. Kannaiya Raja, Secure Intrusion Detection and Attack Measure Selection in Virtual Network Systems. Advances
in Networks. Vol. 1, No. 2, 2013, pp. 26-33. doi: 10.11648/j.net.20130102.12
Abstract: Cloud security is one of most important issues that have attracted a lot of research and development effort in
past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to
deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as
multi-step exploitation, low frequency vulnerability scanning, and compromising identified vulnerable virtual machines as
zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the
Infrastructure-as a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because
cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from
being compromised in the cloud, we propose a multi phase distributed vulnerability detection, measurement, and
countermeasure selection mechanism called NICE, which is built on attack graph based analytical models and
reconfigurable virtual network-based countermeasures. The proposed framework leverages Open Flow network
programming APIs to build a monitor and control plane over distributed programmable virtual switches in order to
significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate
the efficiency and effectiveness of the proposed solution.
Keywords: Performance of Systems, Computer Systems Organization, Network-Level Security and Protection, General,
Communication/Networking and Information Technology
1. Introduction
A recent Cloud Security Alliance (CSA) survey shows
that among all security issues, abuse and nefarious use of
cloud computing is considered as the top security threat[1],
in which attackers can exploit vulnerabilities in clouds and
utilize cloud system resources to deploy attacks. In
traditional data centers, where system administrators have
full control over the host machines, Vulnerabilities can be
detected and patched by the system administrator in a
centralized manner. However, patching known security
holes in cloud data centers, where cloud users usually have
the privilege to control software installed on their managed
VMs, may not work effectively and can violate the Service
Level Agreement (SLA).
Furthermore, cloud users can install vulnerable software
on their VMs, which essentially contributes to loopholes in
cloud security. The challenge is to establish an effective
vulnerability/attack detection and response system for
accurately identifying attacks and minimizing the impact of
security breach to cloud users.
In[2], M. Armbrust et al. addressed that protecting
“Business continuity and services availability” from
service outages is one of the top concerns in cloud
computing systems. In a cloud system where the
infrastructure is shared by potentially millions of users,
abuse and nefarious use of the shared infrastructure
benefits attackers to exploit vulnerabilities of the cloud and
use its resource to deploy attacks in more efficient ways[3].
Such attacks are more effective in the cloud environment
since cloud users usually share computing resources, e.g.,
being connected through the same switch, sharing with the
same data storage and file systems, even with potential
attackers[4]. The similar setup for VMs in the cloud, e.g.,
virtualization techniques, VM OS, installed vulnerable

Advances in Networks 2013; 1(2): 26-33 27
software, networking, etc., attracts attackers to compromise
multiple VMs.
In this paper, we propose Secure Intrusion Detection and
Attack measure exquisite in Virtual Systems to establish a
defense-in-depth intrusion detection framework. For better
attack detection, NICE incorporates attack graph analytical
procedures into the intrusion detection processes. We must
note that the design of NICE does not intend to improve
any of the existing intrusion detection algorithms; indeed,
NICE employs a reconfigurable virtual networking
approach to detect and counter the attempts to compromise
VMs, thus preventing zombie VMs.
In general, NICE includes two main phases: (1) deploy a
lightweight mirroring-based network intrusion detection
agent (NICE-A) on each cloud server to capture and
analyze cloud traffic. A NICE-A periodically scans the
virtual system vulnerabilities within a cloud server to
establish Scenario Attack Graph (SAGs), and then based
on the severity of identified vulnerability towards the
collaborative attack goals, NICE will decide whether or not
to put a VM in network inspection state. (2) Once a VM
enters inspection state, Deep Packet Inspection (DPI) is
applied, and/or virtual network reconfigurations can be
deployed to the inspecting VM to make the potential attack
behaviors prominent.
The rest of paper is organized as follows. Section II
presents the related work. Section III describes system
approach and implementation. System models are
described in Section IV describes the approach to
hardening the network in NICE. The proposed NICE is
presented in Section V and Section VI evaluates NICE in
terms of network performance and security. Finally,
Section VII describes future work and concludes this paper.
2. Related Works
The contributions of NICE are presented as follows:
We devise NICE, a new multi-phase distributed network
intrusion detection and prevention framework in a virtual
networking environment that captures and inspects
suspicious cloud traffic without interrupting users’
applications and cloud services.
NICE incorporates a software switching solution to
quarantine and inspect suspicious VMs for further
investigation and protection. Through programmable
network approaches, NICE can improve the attack
detection probability and improve the resiliency to VM
exploitation attack without interrupting existing normal
cloud services.
NICE employs a novel attack graph approach for attack
detection and prevention by correlating attack behavior and
also suggests effective countermeasures.
NICE optimizes the implementation on cloud servers to
minimize resource consumption. Our study shows that
NICE consumes less computational overhead compared to
proxy-based network intrusion detection solutions.
The area of detecting malicious behavior has been well
explored. The work by Duan et al.[5] focuses on the
detection of compromised machines that have been
recruited to serve as spam zombies. Their approach, SPOT,
is based on sequentially scanning outgoing messages while
employing a statistical method Sequential Probability Ratio
Test (SPRT), to quickly determine whether or not a host
has been compromised. BotHunter[6] detects compromised
machines based on the fact that a thorough malware
infection process has a number of well defined stages that
allow correlating the intrusion alarms triggered by inbound
traffic with resulting outgoing communication patterns.
BotSniffer[7] exploits uniform spatial-temporal behavior
characteristics of compromised machines to detect zombies
by grouping flows according to server connections and
searching for similar behavior in the flow.
An attack graph is able to represent a series of exploits,
called atomic attacks, that lead to an undesirable state, for
example a state where an attacker has obtained
administrative access to a machine. There are many
automation tools to construct attack graph. O. Sheyner et
al.[8] proposed a technique based on a modified symbolic
model checking NuSMV[9] and Binary Decision Diagrams
(BDDs) to construct attack graph. Their model can
generate all possible attack paths; however, the scalability
is a big issue for this solution. P. Ammann et al.[10]
introduced the assumption of monotonicity, which states
that the precondition of a given exploit is never invalidated
by the successful application of another exploit. In other
words, attackers never need to backtrack. With this
assumption, they can obtain a concise, scalable graph
representation for encoding attack tree. X. Ou et al.
proposed an attack graph tool called MulVAL[11], which
adopts a logic programming approach and uses Datalog
language to model and analyze network system. Intrusion
Detection System (IDS) and firewall are widely used to
monitor and detect suspicious events in the network.
However, the false alarms and the large volume of raw
alerts from IDS are two major problems for any IDS
implementations. In order to identify the source or target of
the intrusion in the network, especially to detect multi-step
attack, the alert correction is a must-have tool. The primary
goal of alert correlation is to provide system support for a
global and condensed view of network attacks by analyzing
raw alerts[12]. Many attack graphs based alert correlation
techniques have been proposed recently. L. Wang et al.[13]
devised an in-memory structure, called queue graph (QG),
to trace alerts matching each exploit in the attack graph.
However, the implicit correlations in this design make it
difficult to use the correlated alerts in the graph for analysis
of similar attack scenarios. Roschke et al.[14] proposed a
modified attack-graph-based correlation algorithm to create
explicit correlations only by matching alerts to specific
exploitation nodes in the attack graph with multiple
mapping functions, and devised an alert dependencies
graph (DG) to group related alerts with multiple correlation
criteria. Several solutions have been proposed to select

28 S. Uvaraj et al.: Secure Intrusion Detection and Attack Measure Selection
In Virtual Network Systems
optimal countermeasures based on the likelihood of the
attack path and cost benefit analysis. A. Roy et al.[15]
proposed an attack countermeasure tree (ACT) to consider
attacks and countermeasures together in an attack tree
structure.[16] Proposed a Bayesian attack graph (BAG) to
address dynamic security risk management problem and
applied a genetic algorithm to solve countermeasure
optimization problem.
3. Nice Models
In this section, we describe how to utilize attack graphs
to model security threats and vulnerabilities in a virtual
networked system, and propose a VM protection model
based on virtual network reconfiguration approaches to
prevent VMs from being exploited.
3.1. Threat Model
In our attack model, we assume that an attacker can be
located either outside or inside of the virtual networking
system. The attacker’s primary goal is to exploit vulnerable
VMs and compromise them as zombies. Our protection
model focuses on virtual-network-based attack detection
and reconfiguration solutions to improve the resiliency to
zombie explorations.
Our work does not involve host-based IDS and does not
address how to handle encrypted traffic for attack
detections. Our proposed solution can be deployed in an
Infrastructure- s-a-Service (IaaS) cloud networking system,
and we assume that the Cloud Service Provider (CSP) is
benign. We also assume that cloud service users are free to
install whatever operating systems or applications.
3.2. Attack Graph Model
An attack graph is a modeling tool to illustrate all
possible multi-stage, multi-host attack paths that are crucial
to understand threats and then to decide appropriate
countermeasures [17]. In an attack graph, each node
represents either precondition or consequence of an exploit.
Attack graph is helpful in identifying potential threats,
possible attacks and known vulnerabilities in a cloud
system.
Definition 1 (Scenario Attack Graph). An Scenario Attack
Graph is a tuple SAG= (V, E), where,
• V = NC[ND[NR denotes a set of vertices that
include three types namely conjunction node NC to
represent exploit, disjunction node ND to denote
result of exploit, and root node NR for showing
initial step of an attack scenario.
• E = Epre [Epost denotes the set of directed edges.
An edge e 2 Epre _ ND _ NC represents that ND
must be satisfied to achieve NC. An edge e 2 Epost
_ NC _ ND means that the consequence shown by
ND can be obtained if NC is satisfied.
Node vc NC is defined as a three tuple (Hosts; vul; alert)
representing a set of IP addresses, vulnerability information
such as CVE[18], and alerts related to vc, respectively. ND
behaves like a logical OR operation and contains details of
the results of actions.
NR represents the root node of the scenario attack graph.
For correlating the alerts, we refer to the approach
described in and define a new Alert Correlation Graph
(ACG) to map alerts in ACG to their respective nodes in
SAG. To keep track of attack progress, we track the source
and destination IP addresses for attack activities.
Definition 2 (Alert Correlation Graph).
An ACG is a three tuple ACG = (A; E; P), where
• A is a set of aggregated alerts. An alert a 2 A is a
data structure (src; dst; cls; ts) representing source
IP address, destination IP address, type of the alert,
and timestamp of the alert respectively.
• Each alert a maps to a pair of vertices (vc; vd) in
SAG using map (a) function,
• E is a set of directed edges representing correlation
between two alerts
• P is set of paths in ACG.
Algorithm 1 Alert Correlation
Require: alert ac , SAG, ACG
if (ac is a new alert) then create node ac in ACG
n1 ← vc 2 map (ac)
for all n2 parent(n1) do
create edge (n2,alert,ac )
for all Si containing a do
if a is the last element in Si then
append ac to Si
else
create path Si + 1 ={ subset(Si a)a c}
end if
end for
add ac to n1 alert
end for
end if
return S
Definition 3 (VM State). Based on the information
gathered from the network controller, VM states can be
defined as following:
• Stable: there does not exist any known vulnerability
on the VM.
• Vulnerable: presence of one or more vulnerabilities
on a VM, which remains unexploited.
• Exploited: at least one vulnerability has been
exploited and the VM is compromised.
• Zombie: VM is under control of attacker.
4. Nice System Design
In this section, we first present the system design
overview of NICE and then detailed descriptions of its
components.

4.1. System Design Overview
The proposed NICE framework is illustrated in Figure
1.It shows the NICE framework within one cloud server
cluster. Major components in this framework are
distributed and light-weighted NICE-A on each physical
cloud server, a network controller, a VM profiling server,
and an attack analyzer. The latter three components are
located in a centralized control center connected to
software switches on each cloud server (i.e., virtual
switches built on one or multiple Linux software bridges).
Fig 1. NICE framework within one cloud server cluster
4.2. System Components
In this section we explain each component of NICE.
4.2.1. Nice-A
The NICE-A is a Network-based Intrusion Detection
System (NIDS) agent installed in either dom0 or domU in
each cloud server. It scans the traffic going through Linux
bridges that control all the traffic among VMs and in/out
from the physical cloud servers. NICEA is a software agent
implemented in each cloud server connected to the control
center through a dedicated and isolated secure channel,
which is separated from the normal data packets using
Open Flow tunneling or VLAN approaches. The network
controller is responsible for deploying attack
countermeasures based on decisions made by the attack
analyzer.
4.2.2. VM Profiling
Virtual machines in the cloud can be profiled to get
precise information about their state, services running,
open ports, etc.
VM profiles are maintained in a database and contain
comprehensive information about vulnerabilities, alert and
traffic. The data comes from:
Attack graph generator: while generating the attack
graph, every detected vulnerability is added to its
corresponding VM entry in the database.
NICE-A: the alert involving the VM will be recorded in
the VM profile database.
Network controller: the traffic patterns involving the
VM are based on 5 tuples (source MAC address,
destination MAC address, source IP address, destination IP
address, protocol). We can have traffic pattern where
packets emanate from a single IP and are delivered to
multiple destination IP addresses, and vice-versa.
4.2.3. Attack Analyzer
The major functions of NICE system are performed by
attack analyzer, which includes procedures such as attack
graph construction and update, alert correlation and
countermeasure selection.
The process of constructing and utilizing the Scenario
Attack Graph (SAG) consists of three phases: information
gathering, attack graph construction, and potential exploit
path analysis. With this information, attack paths can be
modeled using SAG.
In summary, NICE attack graph is constructed based on
the following information:
Cloud system information is collected from the node
controller and VM’s Virtual Interface (VIF) information.
Virtual network topology and configuration information
is collected from the network controller, every VM’s IP
address, MAC address, port information, and traffic flow
information.
Vulnerability information is generated by both on
demand vulnerability scanning.
4.2.4. Network Controller
The network controller is a key component to support
the programmable networking capability to realize the
virtual network reconfiguration feature based on Open-
Flow protocol[19]. In NICE, within each cloud server there
is a software switch, for example, Open vSwitch (OVS)[5],
which is used as the edge switch for VMs to handle traffic
in & out from VMs. The network controller is responsible
for collecting network information of current Open Flow
network and provides input to the attack analyzer to
construct attack graphs.
5. Mitigation and Countermeasures
In this section, we present the methods for selecting the
countermeasures for a given attack scenario. The
countermeasure serves the purpose of 1) protecting the
target VMs from being compromised; and 2) making attack
behavior stand prominent so that the attackers’ actions can
be identified.
5.1. Mitigation Strategies
Based on the security metrics defined in the previous
subsection, NICE is able to construct the mitigation
strategies in response to detected alerts. First, we define the
term countermeasure pool as follows:
Definition 4 (Countermeasure Pool)

A countermeasure pool CM = (cm1; cm2; cmn) is a set
of countermeasures. Where
• Cost is the unit that describes the expenses required
to apply the countermeasure in terms of resources
and operational complexity, and it is defined in a
range from 1 to 5, and higher metric means higher
cost;
• intrusiveness is the negative effect that a
countermeasure brings to the Service Level
Agreement (SLA) and its value ranges from the
least intrusive (1) to the most intrusive (5), and the
value of intrusiveness is 0 if the countermeasure
has no impacts on the SLA;
• Condition is the requirement for the corresponding
countermeasure;
• Effectiveness is the percentage of probability
changes of the node, for which this countermeasure
is applied.
Table 1. Possible Countermeasure Types
No. Countermeasure Intrusiveness Cost
1 Traffic redirection 3 3
2 Traffic isolation 4 2
3 Deep packet Inspection 3 3
4 Creating filtering rules 1 2
5 MAC address change 2 1
6 IP address change 2 1
7 Block port 4 1
8 Software patch 5 4
9 Quarantine 5 2
10 Network reconfiguration 0 5
11 Network topology change 0 5
5.2. Countermeasure Selection
Algorithm 2 presents how to select the optimal
countermeasure for a given attack scenario. Input to the
algorithm is an alert, attack graph G, and a pool of
countermeasures CM. The algorithm starts by selecting the
node vAlert that corresponds to the alert generated by a
NICE-A. The countermeasure which when applied on a
node gives the least value of ROI, is regarded as the
optimal countermeasure. Finally, SAG and ACG are also
updated before terminating the algorithm.
Fig 2. Virtual network topology for security evaluation
Algorithm 2 Countermeasure Selection
Require: Alert’s(E; V );CM
Let vAlert = Source node of the Alert
if Distance to Target(vAlert) > threshold then
Update ACG
Return
end if
Let T = Descendant(vAlert) U vAlert
Set Pr(vAlert) = 1
Calculate Risk Prob(T)
Let benefit[jTj; jCMj] = Ø
for each t E T do
for each cm E CM do
if cm:condition(t) then
Pr(t) = Pr(t) (1-cm:effectiveness)
Calculate Risk Prob(Descendant(t))
benefit[t; cm] = Pr(target node): (7)
end if
end for
end for
Let ROI[jTj; jCMj] = Ø
for each t E T do
for each cm E CM do
ROI[t; cm]
end for
end for
Update SAG and Update ACG
return Select Optimal CM(ROI)
Table 2. Vulnerabilities in the virtual networked system
Host Vulnerability Node CVE
Base
Scofe
VM group
LICQ buffer overflow 10
CVE2001-
0439
0.75
MS Video ActiveX
Stack buffer overflow
5
CVE2008-
0015
0.93
GNUC Library loader
flaw
22
CVE2010-
3847
0.69
Admin
Server
MS SMV service
Stack buffer overflow
2
CVE2008-
4050
0.93
Gateway
server
OpenSSL uses
predictable random
variable
15
CVE2008-
0166
0.78
Heap corruption in
OpenSSH
4
CVE2003-
0693
1
Improper cookies
handler in OpenSSH
9
CVE2007-
4752
0.75
Mail
server
Remote code
execution in SMTP
21
CVE2004-
0840
1
Squid port scan 19
CVE2001-
1030
0.75
Web
server
WebDAV
vulnerability in IIS
13
CVE2009-
1535
0.76
6. Performance Evaluation
In this section we present the performance evaluation of
NICE. Our evaluation is conducted in two directions: the
security performance, and the system computing and
network reconfiguration overhead due to introduced
security mechanism.

6.1. Security Performance Analysis
To demonstrate the security performance of NICE, we
created a virtual network testing environment consisting of
all the presented components of NICE.
6.1.1. Environment and Configuration
To evaluate the security performance, a demonstrative
virtual cloud system consisting of public (public virtual
servers) and private (VMs) virtual domains is established
as shown in Figure 2. Cloud Servers 1 and 2 are connected
to Internet through the external firewall.
6.1.2. Attack Graph and Alert Correlation
The attack graph can be generated by utilizing network
topology and the vulnerability information, and it is shown
in Figure 3. As the attack progresses, the system generates
various alerts that can be related to the nodes in the attack
graph. Creating an attack graph requires knowledge of
network connectivity, running services and their
vulnerability information. This information is provided to
the attack graph generator as the input.
Fig 3. Attack graph for the test network.
Definition 5 (VM Security Index). VSI for a virtual
machine k is defined as V SIk = (Vk + Ek) =2, where
• Vk is vulnerability score for VM k. The score is the
exponential average of base score from each
vulnerability in the VM or a maximum 10, i.e., Vk
= minf10; lnPeBaseScore (v) g.
• Ek is exploitability score for VM k. It is the
exponential average of exploitability score for all
vulnerabilities or a maximum 10 multiplied by the
ratio of network services on the VM, i.e., basically,
vulnerability score considers the base scores of all
the vulnerabilities on a VM. The base score depicts
how easy it is for an attacker to exploit the
vulnerability and how much damage it may incur.
The exponential addition of base scores allows the
vulnerability score to incline towards higher base
score values and increases in logarithm-scale based
on the number of vulnerabilities.
Apart from calculating the benefit measurements, we
also present the evaluation based on Return of Investment
(ROI) using (8) and represent a comprehensive evaluation
considering benefit, cost and intrusiveness of
countermeasure. Figure 5 shows the ROI evaluations for
presented countermeasures. Results show that
countermeasures CM2 and CM8 on node 5 have the
maximum benefit evaluation; however their cost and
intrusiveness scores indicate that they might not be good
candidates for the optimal countermeasure and ROI
evaluation results confirm this. The ROI evaluations
demonstrate that CM4 on node 5 is the optimal solution.
Fig 4. Benefit evaluation chart

Fig 5. ROI evaluation chart
6.2. NICE System Performance
We evaluate system performance to provide guidance on
how much traffic NICE can handle for one cloud server
and use the evaluation metric to scale up to a large cloud
system. In a real cloud system, traffic planning is needed to
run NICE, which is beyond the scope of this paper. Due to
the space limitation, we will investigate the research
involving multiple cloud clusters in the future.
Fig 6. CPU utilization of NICE-A
7. Conclusion and Future Work
In this paper, we presented NICE, which is proposed to
detect and mitigate collaborative attacks in the cloud
virtual networking environment. NICE utilizes the attack
graph model to conduct attack detection and prediction.
The proposed solution investigates how to use the
programmability of software switches based solutions to
improve the detection accuracy and defeat victim
exploitation phases of collaborative attacks. The system
performance evaluation demonstrates the feasibility of
NICE and shows that the proposed solution can
significantly reduce the risk of the cloud system from being
exploited and abused by internal and external attackers.
NICE only investigates the network IDS approach to
counter zombie explorative attacks. In order to improve the
detection accuracy, host-based IDS solutions are needed to
be incorporated and to cover the whole spectrum of IDS in
the cloud system. This should be investigated in the future
work. Additionally, as indicated in the paper, we will
investigate the scalability of the proposed NICE solution
by investigating the decentralized network control and
attack analysis model based on current study.
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