Biologically inspired defenses against computer viruses

Biologically Inspired Defenses Against Computer Viruses
Jeffrey O. Kephart, Gregory B. Sorkin,
William C. Arnold, David M. Chess,
Gerald J. Tesauro, and Steve R. White
High Integrity Computing Laboratory
IBM Thomas J. Watson Research Center
Yorktown Heights NY 10598
Abstract
Today's anti-virus technology, based largely on
analysis of existing viruses by human experts,
is just barely able to keep pace with the more
than three new computer viruses that are writ-
ten daily. In a few years, intelligent agents nav-
igating through highly connected networks are
likely to form an extremely fertile medium for
a new breed of viruses. At IBM, we are de-
veloping novel, biologically inspired anti-virus
techniques designed to thwart both today's and
tomorrow's viruses. Here we describe two of
these: a neural network virus detector that
learns to discriminate between infected and un-
infected programs, and a computer immune
system that identifies new viruses, analyzes
them automatically, and uses the results of
its analysis to detect and remove all copies of
the virus that are present in the system. The
neural-net technology has been incorporated
into IBM's commercial anti-virus product; the
computer immune system is in prototype.
1 Introduction
Each day, an army of perhaps a few hundred virus writers
around the world produces three or more new computer
viruses.1
An army of comparable size, the anti-virus
software developers (representing an approximately $100
million per year industry), works feverishly to analyze
these viruses, develop cures for them, and frequently dis-
tribute software updates to users.
Currently, the battle is roughly even. Our statistics,
based on observation of a sample population of several
hundred thousand machines for several years [Kephart
and White, 1993; Kephart et a/., 1993], suggest that in
medium to large businesses roughly 1% of all computers
become infected during any given year. The world's com-
puter population has been inconvenienced, but despite
dire predictions [Tippett, 1991] it has not been incapaci-
tated. Most of the anti-virus products in common usage
have been reasonably effective in detecting and remov-
ing viruses. Within our sample population, only 10%
l
This figure is based on the number of distinct new viruses
that have been received by us during the last year.
of all known viruses (about 360 of 4000 at the time of
writing) have been observed "in the wild" — in real inci-
dents. Several viruses that used to be relatively common
now qualify for inclusion on an endangered species list.
Today, computer viruses are a manageable nuisance.
Several worrisome trends threaten to turn the balance
in the favor of computer virus authors. First, the rate
at which new viruses are created, already on the verge
of overwhelming human experts, has the potential to in-
crease substantially. Second, continued increases in in-
terconnectivity and interoperability among the world's
computers, designed to benefit computer users, are likely
to be a boon to DOS and Macintosh viruses as well. The-
oretical epidemiological studies indicate that the rate at
which computer viruses spread on a global scale can be
very sensitive to the rate and the degree of promiscuity
of software exchange [Kephart and White, 1991; 1993;
Kephart et al.} 1993; Kephart, 1994b]. Anticipated in-
creases in both factors threaten to increase substantially
the speed of spread and the pervasiveness of these tra-
ditional types of virus. In addition, mobile intelligent
agents [Chess et a/., 1995; Harrison et a/., 1994] will soon
navigate the global network, potentially serving as a fer-
tile medium for a new breed of rapidly-spreading virus
that exploits the itinerancy of its host by leaving behind
copies of itself wherever its host goes. Traditional meth-
ods of detecting and removing viruses, which rely upon
expert analysis by humans and subsequent distribution
of the cure to users, would be orders of magnitude too
slow to deal with viruses that spread globally within days
or hours.
To address these problems, we have developed a va-
riety of biologically inspired anti-virus algorithms and
techniques that replace many of the tasks tradition-
ally performed by human virus experts, thus permitting
much faster, automatic response to new viruses.
The term "computer virus", coined by Adleman in
the early 1980's [Cohen, 1987], is suggestive of Btrong
analogies between computer viruses and their biological
namesakes. Both attach themselves to a small functional
unit (cell or program) of the host individual (organism
or computer), and co-opt the resources of that unit for
the purpose of creating more copies of the virus. By us-
KEPHART,ETAL 985

ing up materials (memory3
) and energy (CPU3
), viruses
can cause a wide spectrum of malfunctions in their hosts.
Even worse, viruses can be toxic. In humans, diptheria
is caused by a toxin produced by virally-infected bac-
teria [Levine, 1992]. Some computer viruses are simi-
larly toxic, being deliberately programmed to cause se-
vere harm to their hosts. One notorious example, the
Michelangelo virus, destroys data on a user's hard disk
whenever it is booted on March 6th.
It is therefore natural to seek inspiration from de-
fense mechanisms that biological organisms have evolved
against diseases. The idea that biological analogies
might be helpful in defending computers from computer
viruses is not original to us [Murray, 1988]. But to our
knowledge we are the first to take these analogies seri-
ously, to deliberately design and implement anti-virus
technology that is inspired by biology, and incorporate
it into a commercial anti-virus product.
First, we will briefly describe what computer viruses
are, how they replicate themselves, and why their pres-
ence in a system is undesirable. Then, we shall describe
the typical procedures used by human experts to analyze
computer viruses, and explain why these methods are
unlikely to remain viable a few years from now. Then, we
shall discuss two complementary anti-virus techniques
that are inspired by biological systems that learn: a
neural-network virus detector and a computer immune
system.
2 Background
2.1 Computer viruses and worms
Computer viruses are self-replicating software entities
that attach themselves parasitically to existing pro-
grams. They are endemic to DOS, Macintosh, and other
microcomputer systems. When a user executes an in-
fected program (an executable file or boot sector), the
viral portion of the code typically executes first. The
virus looks for one or more victim programs to which it
has write access (typically the same set of programs to
which the user has access), and attaches a copy of itself
(perhaps a deliberately modified copy) to each victim.
Under some circumstances, it may then execute a pay-
load, such as printing a weird message, playing music,
destroying data, etc. Eventually, a typical virus returns
control to the original program, which executes normally.
Unless the virus executes an obvious payload, the user
is unlikely to notice that anything is amiss, and will be
completely unaware of having helped a virus to repli-
cate. Viruses often enhance their ability to spread by
establishing themselves as resident processes in memory,
persisting long after the infected host finishes its execu-
tion (terminating only when the machine is shut down).
As resident processes, they can monitor system activity
2
The Jerusalem virus increases the size of an executable
by 1813 bytes each time it infects it, eventually causing it to
be too large to be loaded into memory [Highland, 1990].
3 The Internet worm caused the loads on some Unix ma-
chines to increase by two orders of magnitude [Eichin, 1989;
Spafford, 1989].
continually, and identify and infect executables and boot
sectors as they become available.
Over a period of time, this scenario is repeated, and
the infection may spread to several programs on the
user's system. Eventually, an infected program may be
copied and transported to another system electronically
or via diskette. If this program is executed on the new
system, the cycle of infection will begin anew. In this
manner, computer viruses spread from program to pro-
gram, and (more slowly) from machine to machine. The
most successful PC DOS viruses spread worldwide on a
time scale of months [Kephart and White, 1993].
Worms are another form of self-replicating software
that are sometimes distinguished from viruses. They
are self-sufficient programs that remain active in mem-
ory in multi-tasking environments, and they replicate by
spawning copies of themselves. Since they can determine
when to replicate (rather than relying on a human to ex-
ecute an infected program), they have the potential to
spread much faster than viruses. The Internet worm of
1988 is said to have spread to several thousand machines
across the United States in less than 24 hours [Eichin,
1989; Spafford, 1989],
2.2 V i r u s d e t e c t i o n , r e m o v a l a n d analysis
Anti-virus software seeks to detect all viral infections on
a given computer system and to restore each infected
program to its original uninfected state, if possible.
There are a variety of complementary anti-virus tech-
niques in common usage; taxonomies are given in [Spaf-
ford, 1991; Kephart et a/., 1993]. Activity monitors alert
users to system activity that is commonly associated
with viruses, but only rarely associated with the behav-
ior of normal, legitimate programs. Integrity manage-
ment systems warn the user of suspicious changes that
have been made to files. These two methods are quite
general, and can be used to detect the presence of hith-
erto unknown viruses in the system. However, they are
not often able to pinpoint the nature or even the loca-
tion of the infecting agent, and they can sometimes flag
or prevent legitimate activity, disrupting normal work or
leading the user to ignore their warnings altogether.
Virus scanners search files, boot records, memory, and
other locations where executable code can be stored for
characteristic byte patterns (called "signatures") that
occur in one or more known viruses. Providing much
more specific detection than activity monitors and in-
tegrity management systems, scanners are essential for
establishing the identity and location of a virus. Armed
with this very specific knowledge, disinfectors, which re-
store infected programs to their original uninfected state,
can be brought into play. The drawback of scanning and
repair mechanisms is that they can be applied only to
known viruses, or variants of them. Furthermore, each
individual virus strain must be analyzed in order to ex-
tract both a signature and information that permits a
disinfector to remove the virus. Scanners and disinfec-
tors require frequent updates as new viruses are discov-
ered, and the analysis can entail a significant amount of
effort on the part of human virus experts.
Whenever a new virus is discovered, it is quickly dis-
986 INVITED SPEAKERS

tributed among an informal, international group of anti-
virus experts. Upon obtaining a sample, a human expert
disassembles the virus and then analyzes the assembler
code to determine the virus's behavior and the method
that it uses to attach itself to host programs. Then, the
expert selects a "signature" (a sequence of perhaps 16 to
32 bytes) that represents a sequence of instructions that
is guaranteed to be found in each instance of the virus,
and which (in the expert's estimation) is unlikely to be
found in legitimate programs. This signature can then
be encoded into the scanner. The attachment method
and a description of the machine code of the virus can
be encoded into a verifier, which verifies the identity of
a virus that has been found by the scanner. Finally, a
reversal of the attachment method can be encoded into
a disinfector.
Virus analysis is tedious and time-consuming, some-
times taking several hours or days, and even the best
experts have been known to select poor signatures —
ones that cause the scanner to report false positives on
legitimate programs. Alleviation of this burden is by
itself enough to warrant a serious attempt to automate
virus analysis. The anticipated speed with which viruses
of the future may spread is an even stronger argument
in favor of endowing anti-virus software with the ability
to deal with new viruses on its own.4
The rest of this
paper describes two techniques for achieving this goal.
3 Generic Detection of Viruses
Two methods of computer virus identification have al-
ready been introduced: the overly broad, ex post facto
detection provided by activity monitors and integrity
management systems, and the overly specific detection
offered by virus scanners. Somewhere in between is the
ideal "generic detector": taking a program's code as in-
put, it determines whether the program is viral or non-
viral. Perfect generic detection is an algorithmically "un-
decidable" problem: as observed by [Cohen, 1987], it
is reducible to the halting problem. However, imper-
fect generic detection that is good in practice is possi-
ble, and is naturally viewed as a problem in automatic
pattern classification. Standard classification techniques
encompass linear methods and non-linear ones such as
nearest-neighbor classification, decision trees, and multi-
layer neural networks.
Within the problem of the generic detection of viruses,
detection of "boot sector viruses" is both an important
and relatively tractable sub-problem. A boot sector is
a small sequence of code that tells the computer how to
"pick itself up by its bootstraps". For IBM-compatible
PC's, boot sectors are exactly 512 bytes long; their main
function is to load and execute additional code stored
elsewhere.
4
At the very least, anti-virus software must handle a ma-
jority of viruses well enough to prevent them from spreading.
For the foreseeable future, it will continue to be important for
human virus experts to analyse carefully any viruses that ap-
pear in the wild to corroborate the results of the automated
analysis and to determine any side effects that the virus may
cause in infected systems.
Although there are over 4,000 different file-infecting
viruses and only about 250 boot-sector viruses, of the
20 viruses most commonly seen 19 are boot viruses, and
account for over 80% of all virus incidents. Boot viruses
similarly dominate the rolls of newly observed viruses, so
an ability to detect new boot sector viruses is significant
in the war against viruses.
Detecting boot viruses is a relatively limited pattern
classification task. Legitimate boot sectors all perform
similar functions. Viral boot sectors also all perform
similar functions, before passing control to a legitimate
boot sector loaded from elsewhere.
For this application, false positives are critical. False
negatives mean missed viruses, and since viruses occur
fairly rarely, so will false negatives. Also, if a classifier
does let a virus slip by, the outcome is no worse than if
no virus protection were in place. On the other hand,
false positives can occur any time, and will leave a user
worse off than she would have been without virus protec-
tion. Moreover, a false positive on one legitimate boot
sector will mean false-positive events on thousands of
computers. False positives are not tolerable.
Nearest-neighbor classification might seem to be a
simple, attractive approach to the classification of legiti-
mate and viral boot sectors. Natural measures of the dif-
ference between two boot sectors include the Hamming
distance between them (considered as 512-element vec-
tors), or the edit distance [Crochemore, 1994] between
them (considered as text strings). To classify a new boot
sector, the procedure would find the "nearest" of the 250
known boot sector viruses and 100 legitimate boot sec-
tors (a representative if not comprehensive set that we
have collected), and classify the new boot sector as viral
if its nearest neighbor is viral, legitimate if its nearest
neighbor is legitimate.
Unfortunately, nearest-neighbor classification per-
forms poorly for this problem. A viral boot sector can be
just a short string of viral code written over a legitimate
boot sector, so in any overall comparison, the virus will
be more similar to the legitimate boot sector it happened
to overwrite than to any other virus. This says that what
makes viral boot sectors viral is not any overall quality
but the presence of specific viral functions.
These functions can be used to construct a virus classi-
fier. For example, one common action is for a virus to re-
duce the apparent size of available memory so that space
taken up by the virus will not be noticed. Although this
action may be variously implemented in machine code,
most machine code implementations match one of a few
simple patterns. (A fictitious pattern typifying the form
is C31B****AC348F**90D3D217 — about 10 fixed bytes
and some wildcards.) Of the viruses that lower mem-
ory in less conventional ways, most still contain a 2-byte
pattern weakly indicative of the same function, but more
prone to false positives. Similar strong and weak pat-
terns describe other common viral functions.
Using expert knowledge of viral and non-viral boot
sectors and several days of extensive experimentation,
we hand-crafted an ad hoc classifier (see Figure 1). The
classifier scans a boot sector for the presence of patterns
that provide strong or weak evidence for any of four viral
KEPHART, ET AL 987

Figure 1: A hand-crafted multi-level classifier network.
Eliminating the "MAX" boxes produces a more conven-
tional neural network, but it is inferior, even when the
seven weights are optimized.
functions. One point is credited for weak evidence, and
two points for strong evidence. A boot sector is classified
as viral if its total score is 3 or higher. This classifier per-
formed well on the 350 examples, with a false-negative
rate of about 18% and a false-positive rate too small to
measure over the 100 negative examples. That is, 82%
of viruses were detected, and no legitimate boot sector
was classified as viral.
We hoped to develop a procedure for automatically-
constructing a virus classifier, using similar features as
inputs to a neural network. Since the ad hoc classifier in-
corporated knowledge of all of the available boot sectors,
there was a possibility that it suffered from overfitting,
in which case it would generalize poorly on new data. It
would be much easier to assess the generalization perfor-
mance of an automatically constructed classifier. Also,
we hoped that algorithmic extraction of features and
optimization of network weights might give even better
classification performance, especially in the false-positive
measure. Finally, we believed that an automated proce-
dure would adapt much more readily to new trends in
boot sector viruses. If substantially new types of boot
sector viruses became common, we could simply retrain
the classifier — a much easier task than hacking on an
ad hoc classifier, or re-writing it from scratch.
Essentially, what we did was this. We extracted a set
of 3-byte strings, or "trigrams", appearing frequently in
viral boot sectors but infrequently in legitimate ones.
The presence (1) or absence (0) of the strings defined
the input vector to a single-layer neural network. (See
Figure 2.) Its weights were "trained" over about half
the examples, and the resulting network's performance
tested on the other half. During the development of the
automatic classifier, we encountered novel challenges in
feature pruning and ill-defined learning that we think
represent interesting general issues in learning. These
will be introduced in the course of a more detailed de-
scription of the classifier's construction.
3.1 F e a t u r e selection *.
The first step in the construction was the selection of
byte strings to act as features. Where a human expert
is able to use high-level understanding of viruses, knowl-
edge of machine code, and natural intelligence to select
complex feature patterns containing wildcards, for algo-
rithmic feature generation we contented ourselves with
simple 3-byte features. A training set with 150 512-byte
viral boot sectors includes 76,500 "trigrams", of which
typically 25,000 are distinct.
This is where the first challenge, feature pruning,
comes in. A well known principle in machine learning
states that the number of training examples must be
considerably larger than the number of adjustable para-
meters to reliably give good generalization to test exam-
ples [Hertz et al, 1991]. With 150 viral and 45 non-viral
training examples, a network must have well fewer than
195 weights — say about 50 — implying a lesser or equal
number of inputs. Somehow the 25,000 trigrams must be
winnowed down to 50.
Since what is desired are trigrams that are indicative
of viral as opposed to legitimate behavior, it is natural
to remove trigrams appearing too frequently in legiti-
mate boot sectors. Eliminating all trigrams which ap-
pear even once in the 45 legitimate training examples
reduces the 25,000 candidate trigrams by only about 8%.
On the reasoning that trigrams occurring frequently in
PC programs in general are analogous to the English
word "the" and not salient features, further winnow-
ing can be done. Eliminating trigrams with frequency
over 1/200,000 (occurring on average more than once
in 200,000 bytes) again reduces the number about 8%,
leaving about 21,000 of the original 25,000 candidate fea-
tures. Much more drastic pruning is required.
It is provided by selecting trigram features which fig-
ure importantly in the viral training set. One way to do
this would be to select trigrams occurring at least some
number of times in the viral training set, but this leaves
some viral samples unrepresented by any trigrams. A
better approach comes from selecting a "cover" of tri-
grams: a set of trigrams with at least one trigram repre-
senting each of the viral samples. In fact, we can afford
something close to a 4-cover, so that each viral sample
is represented by 4 different trigrams in the set. (A few

samples have fewer than 4 representatives in the full set
of 21,000 trigrams, in which case they are only required
to be 3-covered, 2-covered, or singly covered, as possi-
ble.) Four-covering produces a set of about 50 trigram
features, few enough to be used as input to a neural net.
(Even so, a complete two-layer network with h hidden
nodes would have h times as many weights as inputs,
which here is prohibitive even for an h of 2 or 3; this is
why we used a single-layer network.)
Reassuringly, most of the trigrams were substrings of
or otherwise similar to the more complex patterns of
the ad hoc classifier. However, there were a few trigrams
that could not be related to any of these patterns, and on
expert inspection they turned out to define a meaningful
new feature class.
3.2 Classifier t r a i n i n g a n d p e r f o r m a n c e
By construction, the selected trigrams are very good fea-
tures: within the training set, no legitimate boot sector
contains any of them, and most of the viral boot sectors
contain at least 4. Paradoxically, the high quality of the
features poses the second challenge, what we have called
the problem of ill-defined learning. Since no negative ex-
ample contains any of the features, any "positive" use of
the features gives a perfect classifier.
Specifically, the neural network classifier of Figure 2
with a threshold of 0 and any positive weights will give
perfect classification on the training examples, but since
even a single feature can trigger a positive, it may be
susceptible to false positives on the test set and in real-
world use. The same problem shows up as an instabil-
ity when the usual back-propagation [Rumelhart et al,
1986] training procedure is used to optimize the weights:
larger weights are always better, because they drive the
sigmoid function's outputs closer to the asymptotic ideal
values of -1 and 1.
In fact all that will keep a feature's ideal weighting
from being infinite is the feature's presence in some neg-
ative example. Since none of the features were present in
any negative example, our solution was to introduce new
examples. One way is to add a set of examples defined
by an identity matrix. That is, for each feature in turn,
an artificial negative example is generated in which that
feature's input value is 1 and all other inputs are 0. This
adds one artificial example for each trigram feature; it
might be better to emphasize features which are more
likely to appear by chance.
To do so, we used 512 bytes of code taken from the
initial "entry point" portions of many PC programs to
stand in as artificial legitimate boot sectors; the thought
was that these sections of code, like real boot sectors,
might be oriented to machine setup rather than perfor-
mance of applications. Of 5,000 such artificial legitimate
boot sectors, 100 contained some viral feature. (This is
about as expected. Each selected trigram had general-
code frequency of under 1/200,000, implying that the
chance of finding any of 50 trigrams among 512 bytes is
at most 13%; the observed rate for the artificial boot sec-
tors was 5%.) Since not all of the 50 trigrams occurred
in any artificial boot sector, we used this approach in
combination with the "identity matrix" one.
At this point the problem is finally in the form of
the most standard sort of (single-layer) feed-forward
neural network training, which can be done by back-
propagation. In typical training and testing runs, we
find that the network has a false-negative rate of 10-
15%, and a false-positive rate of 0.02% as measured on
artificial boot sectors.5
(Given the trigrams' frequencies
of under 1/200,000, if their occurrences were statistically
independent, the probability of finding two within some
512 bytes would be at most 0.8%.) Consistent with the
0.02% false-positive rate, there were no false positives on
any of the 100 genuine legitimate boot sectors.
There was one eccentricity in the network's learning.
Even though all the features are indicative of viral be-
havior, most training runs produced one or two slightly
negative weights. We are not completely sure why this
is so, but the simplest explanation is that if two features
were perfectly correlated (and some are imperfectly cor-
related), only their total weight is important, so one may
randomly acquire a negative weight and the other a cor-
respondingly larger positive weight.
For practical boot virus detection, the false-negative
rate of 15% or less and false-positive rate of 0.02% are an
excellent result: 85% of new boot sector viruses will be
detected, with a tiny chance of false positives on legit-
imate boot sectors. In fact the classifier, incorporated
into IBM Antivirus, has caught several new viruses.
There has also been at least one false positive, on a
"security" boot sector with virus-like qualities, and not
fitting the probabilistic model of typical code. Rather
than specifically allowing that boot sector, less than an
hour of re-training convinced the neural network to clas-
sify it negatively; this may help to reduce similar false
positives.
Of the 10 or 15% of viruses that escape detection,
most do so not because they fail to contain the feature
trigrams, but because the code sections containing them
are obscured in various ways. If the obscured code is cap-
tured by independent means, the trigrams can be passed
on to the classifier and these viruses too will be detected.
4 A Computer Immune System
Although generic virus detection works well for boot-
sector viruses, and may eventually prove useful for file
infectors as well, at least two drawbacks are inherent in
the technique:
1. New viruses can be detected only if they have a
sufficient amount of code in common with known
viruses.
2. The method is appropriate for viral detection only;
it is incapable of aiding in the removal of a virus
from an infected boot sector or file. The only way
5 Comparison of this classifier's 85% detection rate on test
data with the 82% rate of the hand-crafted one is more fa-
vorable than the numbers suggest. The rate for the neural
net was measured over an independent test set, where for the
hand-crafted detector there was no training-testing division.
Measured over all examples (and especially if trained over all
examples), the network's detection rate exceeds 90%.
KEPHART.ETAL 989

to eliminate the infection is to erase or replace the
infected boot sector or file.
The generic classifier could be viewed as an analog of the
"innate", or non-adaptive, non-specific immune system
that is present in both vertebrates and lower animals.
One important component of this innate immunity can
be viewed as a sort of generic classifier system, in which
the features on which recognition is based include:
1. the presence of certain proteins that are always
present on self-cells, but usually not on foreign
cells,6
2. the presence of double-strand RNA, which appears
in much larger concentrations in a particular class
of viruses than it does in mammalian cells [Marrack,
1993], and
3. the presence of a peptide that begins with an un-
usual amino acid (formyl methionine) that is pro-
duced copiously by bacteria, but only in minute
amounts by mammals [Marrack, 1993].
This generic classification is coupled with a generic re-
sponse to a pathogen that either disables it or kills it.
However, vertebrates have evolved a more sophisti-
cated, adaptive immune system that works in concert
with the innate immune system, and is based on recog-
nition of specific pathogens.7
It exhibits the remarkable
ability to detect and respond to previously unencoun-
tered pathogens, regardless of their degree of similarity
to known pathogens. This is precisely the sort of defen-
sive capability that we seek against computer viruses.
Figure 3 provides an overview of our design for an
adaptive computer immune system. The immune system
responds to virus-like anomalies (as identified by vari-
ous activity and integrity monitors) by capturing and
analyzing viral samples. From its analysis, it derives
the means for detecting and removing the virus. Many
components of the computer immune system are work-
ing in the laboratory, and are providing useful data that
is incorporated into I B M Antivirus, IBM's commercial
anti-virus product.
The remainder of this section will be devoted to a dis-
cussion of the various components of the immune system
design, along with their relationship to analogous bio-
logical principles. Further exploration of some biological
analogies can be found in [Kephart, 1994a]. First, we
shall consider the set of components that are labeled
as being currently in I B M Antivirus: anomaly detec-
tion, scanning for known viruses, and removal of known
viruses. Then, we shall discuss some of the components
6
These proteins inactivate complement, a class of proteins
that bind to cells, and attract the attention of other compo-
nents of the immune system, which kill the cell [Janeway,
1993].
7
This extra sophistication pits the quick adaptability of
the immune system, which occurs within a single individual
over the course of a few days, against the similarly quick
evolutionary adaptability of pathogens (due to their short
life-cycles). Due to their much slower life-cycles, it is doubtful
that vertebrates could hold their own if their immune systems
had to rely on evolution alone.
that are labeled as being currently in the virus lab: sam-
ple capture using decoys, algorithmic virus analysis, and
signature extraction. These components are all function-
ing prototypes. Finally, we shall discuss a mechanism by
which one machine can inform its neighbors about viral
infections.
4 . 1 A n o m a l y d e t e c t i o n
The fundamental problem faced by both biological and
computer immune systems is to distinguish between ma-
lignant and benign entities that enter the individual.
Due to the high degree of stability of body chemistry
in individual vertebrates during their lifetimes, their im-
mune systems can replace this difficult task with the
much simpler one of distinguishing self from non-self.
This is a nice hack, because "self" is much easier to define
and recognize than "benign". The biological immune
system can simply implement the xenophobic strategy:
"Know thyself (and reject all else)." This strategy errs
on the side of false positives (i.e. false rejection of be-
nign entities), but except in cases of blood transfu-
sions and organ transplants, these mistakes are of little
consequence.8
In computers, the same xenophobic strategy is an im-
portant component of anomaly detection. Integrity mon-
itors can use checksums or other methods9
to determine
whether an existing executable has changed. However,
this is only a partial solution. The nature of "self", i.e.
the collection of software on an individual computer, is
continually shifting over time — much more so than in
biological organisms. People continually add new soft-
ware to their system, and update existing software by
buying new versions or compiling new source code. The
fact that an executable is new or has changed is not
nearly enough to warrant suspicion. An array of other
monitors and heuristics employ a complementary "Know
thine enemy" strategy: the nature of the anomaly must
be strongly indicative of a virus. Some components of
the anomaly detector trigger on suspicious dynamical be-
haviors (such as one process writing to an executable or
boot record, or unusual sequences of operating system
calls, perhaps involving interception of particular inter-
rupts); others trigger on static properties having to do
with the exact nature of a change that has been identi-
fied by the integrity monitor.
4.2 S c a n n i n g for k n o w n viruses
If the anomaly detector has been triggered, the system
is scanned for all known viruses. Since there are cur-
rently at least 4000 known PC DOS viruses, this means
that exact or slightly inexact matches to approximately
4000 signatures, each in the range of roughly 16 to 32
bytes long, are searched in parallel. This is in itself an
interesting string matching problem, and efficient search
methods are an active area of research for us. Much
8 Another important class of false positives are auto-
immune reactions, which are sometimes induced by biochem-
ical changes that occur at puberty (thus changing the nature
of "self").
9
A novel method for integrity monitoring that is based on
a close analogy to T cells is described in [Forrest et a/., 1994].

more impressive than any string matching algorithm we
could ever hope to devise, however, is the parallel search
carried out by the vertebrate immune system, in which
roughly 10 million different types of T-cell receptors and
100 million different types of antibodies and B-cell re-
ceptors are continually patrolling the body in search of
antigen [Janeway, 1993]. Just as a computer virus scan-
ner recognizes viruses on the basis of (perhaps inexact)
matches to a fragment of the virus (the signature), T-cell
and B-cell receptors and antibodies recognize antigen by
binding (strongly or weakly, depending on the exactness
of the match) to fragments of the antigen (consisting of
linear sequences of 8 to 15 amino acids, in the case of T
cells [Janeway, 1993]).
Matching to fragments rather than the entire antigen
is a physical necessity in the biological immune system;
in computers, this strategy is not absolutely necessary,
but it has some important advantages. Matching to frag-
ments is more efficient in time and memory, and permits
the system to recognize slight variants, particularly when
some mismatches are tolerated. These issues of efficiency
and variant recognition are relevant for biology as well.
For both biological and computer immune systems, an
ability to recognize variants is essential because viruses
tend to mutate frequently. If an exact match were re-
quired, immunity to one variant of a virus would confer
no protection against a slightly different variant. Simi-
larly, vaccines would not work, because they rely on the
biological immune system's ability to synthesize antibod-
ies to tamed or killed viruses that are similar in form to
the more virulent one that the individual is being immu-
nized against.
4.3 V i r u s r e m o v a l
In the biological immune system, if an antibody encoun-
ters antigen, they bind together, and the antigen is effec-
tively neutralized. Thus recognition and neutralization
of the intruder occur simultaneously. Alternatively, a
killer T cell may encounter a cell that exhibits signs of
being infected with a particular infecting agent, where-
upon it kills the host cell. This is a perfectly sensible
course of action, because an infected host cell is slated
to die anyway, and its assassination by the killer T cell
prevents the viral particles from reaching maturation.
A computer immune system can take the same basic
approach to virus removal: it can erase or otherwise inac-
tivate an infected program. However, an important dif-
ference between computer viruses and biological viruses
raises the possibility of a much gentler alternative.
In biological organisms, most infected cells would not
be worth the trouble of saving even if this were possible,
because cells are an easily-replenished resource. °
In contrast, each of the applications run by a typical
computer user is unique in function and irreplaceable
(unless backups have been kept, of course). Since a user
would be likely to notice any malfunction, all but the
most ill-conceived computer viruses attach themselves
to their host in such a way that they do not destroy its
10
Neurons are a notable exception, but they are protected
from most infections by the blood-brain barrier [Seiden,
1995].
function. Viruses tend to merely rearrange or reversibly
transform their hosts. Thus an infected program is usu-
ally expressible as a reversible transformation of the un-
infected original.
When the scanner identifies a particular program as
being infected with a particular virus, the first step in
our removal procedure is to verify that the virus is iden-
tical to a known strain. Verification is based upon check-
sums of regions of viral code that are known to be invari-
ant (perhaps after an appropriate decryption operation)
across different instances of the virus. The exact loca-
tion and structure of the virus must have been derived
beforehand, and expressed in terms of a language under-
stood by the verification algorithm. If the verification
does not succeed, an attempt to remove the virus by this
means is considered too risky, and another more generic
virus removal method (beyond the scope of this paper)
is brought into play. If the verification succeeds, a repair
algorithm carries out the appropriate sequence of steps
required for removing that virus, expressed in a simple
repair language. The sequence of steps is easily derived
from an analysis of the locations (and transformations,
if any) of all of the portions of the original host.
Although the analysis required to extract verifica-
tion and removal information has traditionally been per-
formed by human experts, we shall discuss in a later sub-
section an automated technique for obtaining this infor-
mation.
4.4 D e c o y s
Suppose that the anomaly detector has found evidence
of a virus, but that the scanner cannot identify it as any
of the known strains. Most current anti-virus software
will not be able to recover the host program unless it was
deliberately stored or analyzed11
prior to becoming in-
fected. Ideally, one would like to have stronger evidence
that the system really is infected, and to know more
about the nature of the virus, so that all instances of it
(not just the one discovered by the anomaly detector)
can be found and eliminated from the system.
In the computer immune system, the presence of a
previously unknown virus in the system can be estab-
lished with much greater certainty than can be provided
by the anomaly detector. The idea is to lure the virus
into infecting one or more members of a diverse suite
of "decoy" programs. Decoys are designed to be as at-
tractive as possible to those types of viruses that spread
most successfully. A good strategy for a virus to follow
is to infect programs that are touched by the operat-
ing system in some way. Such programs are most likely
to be executed by the user, and thus serve as the most
successful vehicle for further spread. Therefore, the im-
mune system entices a putative virus to infect the decoy
programs by executing, reading, writing to, copying, or
otherwise manipulating them. Such activity attracts the
attention of many viruses that remain active in memory
even after they have returned control to their host. To
11
Generic disinfection methods can store a small amount
of information about an uninfected program, and use this
information to help reconstruct it if it subsequently becomes
infected.

catch viruses that do not remain active in memory, the
decoys are placed in places where the most commonly
used programs in the system are typically located, such
as the root directory, the current directory, and other
directories in the path. The next time the infected file
is run, it is likely to select one of the decoys as its vic-
tim. From time to time, each of the decoy programs is
examined to see if it has been modified. If any have been
modified, it is almost certain that an unknown virus is
loose in the system, and each of the modified decoys
contains a sample of that virus. These virus samples are
stored in such a way that they will not be executed ac-
cidentally. Now they are ready to be analyzed by other
components of the immune system.
The capture of a virus sample by the decoy programs
is somewhat analogous to the ingestion of antigen by
macrophages [Paul, 1991]. Macrophages and other types
of cells break antigen into small peptide fragments and
present them on their surfaces, where they are subse-
quently bound by T cells with matching receptors. A
variety of further events can ensue from this act of bind-
ing, which in one way or another play essential roles in
recognizing and removing the pathogen. Capture of an
intruder by computer decoys or biological macrophages
allows it to be processed into a standard format that
can be interpreted by other components of the immune
system, provides a standard location where those com-
ponents can obtain information about the intruder, and
primes other parts of the immune system for action.
4.5 A u t o m a t i c v i r u s analysis
Typically, a human expert applies a deep understand-
ing of machine instruction sequences to virus analysis.
Sometimes, this is combined with observation of the ef-
fects of the virus on a program.
Our automatic virus analysis algorithm is much less
sophisticated in its knowledge of machine code, but
makes up for this deficiency by making use of more
data: specifically, several samples of the virus. Once
a few samples of the virus have been captured, the al-
gorithm compares the infected decoys with one another
and with the uninfected decoys to yield a precise de-
scription of how the virus attaches to any host. The
description is completely independent of the length and
contents of the host, and to some extent can accommo-
date self-encrypting viruses. A pictorial representation
of one particularly simple infection pattern is presented
in Fig. 4.
Automatic virus analysis provides several useful types
of information:
1. The location of all of the pieces of the original host
within an infected file, independent of the content
and length of the original host. This information
is automatically converted into the repair language
used by the virus removal component of IBM An-
tivirus.
2. The location and structure of all components of the
virus. Structural information includes the contents
of all regions of the virus that are invariant across
different samples. This information has two pur-
poses:
4.6 Automatic signature extraction
The basic goal of automatic signature extraction is to
choose a signature that is very likely to be found in all
instances of the virus, and very unlikely to be found
accidentally in uninfected programs. In other words,
we wish to minimize false negatives and false positives.
False negatives are dangerous because they leave the user
vulnerable to attack. False positives are extremely an-
noying to customers, and so infuriating to vendors of
falsely-accused software that they have led to at least
one lawsuit.
To minimize false negatives, we first start with the
contents of the invariant regions that have been identified
by the automatic virus analysis procedure. However, it
is quite conceivable that not all of the potential variation
has been captured within the samples. As a general rule,
non-executable "data" portions of programs, which can
include representations of numerical constants, character
strings, work areas for computations, etc., are inherently
more likely to vary from one instance of the virus to an-
other than are "code" portions, which represent machine
instructions. The origin of the variation may be internal
to the virus, or a virus hacker might deliberately change
a few data bytes in an effort to elude virus scanners. To
be conservative, "data" areas are excluded from further
consideration as possible signatures. Although the task
of separating code from data is in principle somewhat
ill-defined, there are a variety of methods, such as run-
ning the virus through a debugger or virtual interpreter,
which perform reasonably well.
At this point, there are one or more sequences of in-
variant machine code bytes from which viral signatures
could be selected. We take the set of candidate signa-
tures to be all possible contiguous blocks of 5 bytes found
in these byte sequences, where S is a signature length
that is predetermined or determined by the algorithm
itself. (Typically, S ranges between approximately 12
and 36.) The remaining goal is to select from among the
KEPHART,ETAL 993

candidates one or perhaps a few signatures that are least
likely to lead to false positives.
We have formulated the problem of minimizing the
false positive probability as follows. For each candidate
signature, estimate the probability for it to match a ran-
dom sequence of length S that is generated by the same
probability distribution that generates legitimate soft-
ware on the relevant platform. (Of course, machine code
is written by people or compilers, not probability distrib-
utions, so such a probability distribution is a theoretical
and somewhat ill-defined construct, but we estimate its
statistics from a set of over 10,000 DOS and OS/2 pro-
grams, constituting half a gigabyte of code.) Then, we
select the candidate signature for which the estimated
probability is the smallest.
In slightly more detail, the key steps of the algorithm
are as follows:
1. Form a list of all n-grams (sequences of n bytes;
1 < n < n max) contained in the input data. (n max
is typically 5 or 8.)
2. Calculate the frequency of each such n-gram in the
"self" collection.
3. Use a simple formula that chains together condi-
tional probabilities based on the measured n-gram
frequencies to form a "false-positive" probability es-
timate for each candidate signature, i.e. the prob-
ability that it matches a random S-byte sequence
chosen from code that is statistically similar to
"self.
4. Select the signature with the lowest estimated false-
positive probability.
Characterizations of this method [Kephart and Arnold,
1994] show that the probability estimates are poor on
an absolute scale, due to the fact that code tends to be
correlated on a longer scale than 5 or 8 bytes. However,
the relative ordering of candidate signatures is rather
good, so the method generally selects one of the best
possible signatures. In fact, judging from the relatively
low false-positive rate of the I B M Antivirus signatures
(compared with that of other anti-virus vendors), the al-
gorithm's ability to select good signatures may be better
than that achieved by human experts.
In a sense, the signature extraction algorithm com-
bines elements of outmoded and current theories of how
the vertebrate immune system develops antibodies and
immune-cell receptors to newly encountered antigen.
The template theory, which held sway from the mid-
1930*8 until the early 1960's, was that antibodies and
receptors molded themselves around the antigen. The
clonal selection theory holds that a vast, random reper-
toire of antibodies and receptors is generated, and those
that recognize self are eliminated during the maturation
phase. Of the remaining antibodies and receptors, at
least a few will match any foreign antigen that is en-
countered. The clonal selection theory gained favor in
the 1960's, and is currently accepted [Paul, 1991].
Our automatic signature extraction method starts out
looking like the template theory. Instead of generating
a large random collection of signatures that might turn
out to be useful someday, we take the collection of code
for a particular virus as our starting point in choosing
a signature. However, we do share one important el-
ement with the clonal selection theory: elimination of
self-recognizing signatures. In fact, the automatic sig-
nature extraction method is even more zealous in this
endeavor than clonal selection, in that it only retains
the "best" signature.
4 . 7 I m m u n o l o g i c a l m e m o r y
The mechanisms by which the vertebrate immune system
retains a lifelong memory of viruses to which it has been
exposed are quite complex, and are still the subject of
study and debate.
By contrast, immunological memory is absolutely triv-
ial to implement in computers. During its first encounter
with a new virus, a computer system may be "ill", i.e.
it will devote a fair amount of time and energy (or CPU
cycles) to virus analysis. After the analysis is complete,
the extracted signature and verification/repair informa-
tion can be added to the appropriate known-virus data-
bases. During any subsequent encounter, detection and
elimination of the virus will occur very quickly. In such
a case the computer can be thought of as "immune" to
the virus.
4.8 F i g h t i n g self-replication w i t h
self-replication
In the biological immune system, immune cells with re-
ceptors that happen to match a given antigen reasonably
well are stimulated to reproduce themselves. This pro-
vides a very strong selective pressure for good recogniz-
ers, and by bringing a degree of mutation into play, the
immune cell is generally able to come up with immune
cells that are extremely well-matched to the antigen in
question.
One can view this as a case in which self-replication
is being used to fight a self-replicator (the virus) in
a very effective manner. One can cite a number of
other examples in nature and medical history where this
strategy has been employed, such as the deliberate use
of the myxoma virus in the 1950's to curtail an ex-
ploding rabbit population in Australia [McNeill, 1976;
Levine, 1992].
The self-replicator need not itself be a virus. In
the case of the worldwide campaign against smallpox,
launched by the World Health Organization in 1966,
those who were in close contact with an infected individ-
ual were all immunized against the disease. Thus immu-
nization spread as a sort of anti-disease among smallpox
victims. This strategy was amazingly successful: the last
naturally occurring case of smallpox occurred in Somalia
in 1977 [Bailey, 1975].
We propose to use a similar mechanism, which we call
the "kill signal", to quell viral spread in computer net-
works. When a computer discovers that it is infected, it
can send a signal to neighboring machines. The signal
conveys to the recipient the fact that the transmitter
was infected, plus any signature or repair information
that might be of use in detecting and eradicating the
virus. If the recipient finds that it is infected, it sends
the signal to its neighbors, and so on. If the recipient

is not infected, it does not pass along the signal, but
at least it has received the database updates, effectively
immunizing it against that virus.
Theoretical modeling has shown the kill signal to
be extremely effective, particularly in topologies that
are highly localized or sparsely connected [Kephart and
White, 1993; Kephart, 1994b].
5 Conclusion and Perspective
The development of the generic virus detector and the
computer immune system were primarily motivated by
practical concerns: human virus experts are on the verge
of being overwhelmed, and we need to automate as much
of what they do as possible.
The generic virus detector was incorporated into IBM
Antivirus in May, 1994, and since that time it has suc-
cessfully identified several new boot viruses. It is the
subject of a pending patent. Most of the components
of the computer immune system are functioning as very
useful prototypes in our virus isolation laboratory; we
use them every day to process the large sets of new
viruses that arrive in the mail from other virus experts
around the world. The immune system itself is the sub-
ject of a pending patent, as are several of its components,
including automatic virus analysis and automatic signa-
ture extraction.
Our eventual goal is to incorporate the immune sys-
tem into I B M Antivirus and, a few years from now, in
networks inhabited by itinerant software agents. More
implementation and more invention, guided in part by
the biological metaphor, lie ahead.
Although our primary motivation for developing a
computer immune system is practical, it is interesting
to adopt a more philosophical perspective.
Consider the history of how humans have handled dis-
ease. For millions of years, our sole defense against
infectious disease was our immune system, and it has
done a good job of defending us from most infectious
diseases. When we are suffering from the common cold,
we may experience a few days of discomfort while the im-
mune system figures out how to recognize and eradicate
the virus, but we usually survive the attack. However,
a minority of diseases, like smallpox or AIDS, are not
handled effectively by the immune system. Fortunately,
during the last few centuries, we have made tremendous
advances in our understanding of infectious diseases at
both the macroscopic and microscopic levels, and med-
ical practices based on this understanding now augment
the capabilities of our natural immune system.
A few hundred years ago, disease began to be un-
derstood at the macroscopic level. In 1760, Daniel
Bernoulli, the founder of mathematical physics, was in-
terested in determining whether a particular form of in-
oculation against smallpox would be generally beneficial
or harmful to society. Formulating and solving a math-
ematical model, he found that inoculation could be ex-
pected to increase the average life expectancy by three
years. His work founded the field of mathematical epi-
demiology [Bailey, 1975]. Observational epidemiology re-
ceived a major boost from John Snow, who in 1854 was
able to deduce the origin of a severe cholera outbreak
in London by plotting the addresses of victims on a city
map [Bailey, 1975].
The macroscopic approaches of Snow and Bernoulli
proved fruitful even before bacteria and viruses were
identified as the underlying cause of infectious disease in
the late 19th century. During the 20th century, research
at the microscopic level has supplemented epidemiology.
Electron microscopy and X-ray crystallography brought
the structure of viruses into view in the 1930's, and the
fascinating complexities of their life cycle and biochem-
istry began to be studied intensively in the mid-1940's.
These advances established terra firm a on which math-
ematical epidemiologists could build their models.
Today, epidemiologists, in the detective role pioneered
by John Snow, discover new viruses [Garrett , 1994].
Biochemists, molecular biologists, and geneticists work
to elucidate the secrets of viruses, and to create safe and
effective vaccines for them. Epidemiologists use intuition
and mathematics to develop plans for immunizing popu-
lations with these vaccines. The eradication of smallpox
from the planet in 1977 is probably the greatest triumph
of this multi-disciplinary collaboration.
Interestingly, the history of man's defense against
computer viruses is almost exactly reversed. Computer
viruses were first understood at the microscopic level,
thanks to the pioneering work of Fred Cohen in the
early 1980's [Cohen, 1987]. As soon as the first DOS
viruses began to appear in 1987 [Highland, 1990], they
were dissected in great detail, and the first primitive
anti-virus software was written. It was not until 1990
that the first real attempts were made to understand
the spread of computer viruses from a macroscopic per-
spective [Kephart and White, 1991; 1993; Tippett, 1990;
1991]. Finally, in the mid-1990's, we are proposing to
give computers what humans and other vertebrates have
always relied upon as a first line of defense against dis-
ease: an immune system.
The Center for Disease Control does not get worked up
when a new strain of the common cold sweeps through
a population. Instead, they concentrate their limited
resources on finding cures for horrible diseases such as
AIDS. Currently, the world community of anti-virus re-
searchers (the computer equivalent of the CDC) squan-
ders lots of time analyzing the computer equivalents of
the common cold. Our hope is that a computer immune
system will deal with most of the standard, run-of-the-
mill viruses quietly and effectively, leaving just a small
percentage of especially problematic viruses for human
experts to analyze.
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