Biological models of security for virus propagation in computer networks

S A N J A Y G O E L A N D S T E P H E N F. B U S H
biological models
of security for virus
propagation in
computer networks
Dr. Goel is an assistant professor in the School of
Business and director of research at the Center for
Information Forensics and Assurance at SUNY Albany.
His research interests include distributed computing,
computer security, risk analysis, biological modeling,
and optimization algorithms.
goel@albany.edu
http://www.albany.edu/~goel
Dr. Bush is a researcher at GE Global Research. He con-
tinues to explore novel concepts in complexity and
algorithmic information theory with a spectrum of
applications ranging from network security and low-
energy wireless ad hoc sensor networking to DNA
sequence analysis for bioinformatics.
bushsf@research.ge.com
http://www.research.ge.com/~bushsf
T H I S A R T I C L E D I S C U S S E S T H E S I M I -
larity between the propagation of patho-
gens (viruses and worms) on computer net-
works and the proliferation of pathogens in
cellular organisms (organisms with genetic
material contained within a membrane-
encased nucleus). It introduces several bio-
logical mechanisms which are used in these
organisms to protect against such patho-
gens and presents security models for net-
worked computers inspired by several bio-
logical paradigms, including genomics (RNA
interference), proteomics (pathway map-
ping), and physiology (immune system). In
addition, the study of epidemiological mod-
els for disease control can inspire methods
for controlling the spread of pathogens
across multiple nodes of a network. It also
presents results based on the authors’
research in immune system modeling.
The analogy between computers and communication
networks and living organisms is an enticing paradigm
that researchers have been exploring for some time. In
1984 Fred Cohen, in his Ph.D. dissertation, first put
the term “computer virus” into print, although there
he credits Len Adleman with coining the term used to
describe the malicious pieces of code that can prolifer-
ate on a network and infect multiple computers. Since
then, advances in bioinformatics (that is, the modeling
of biological processes as well as storage, retrieval, and
analysis of biological data through the use of informa-
tion technology) have helped to define these analogies
more precisely, to the point where results in bioinfor-
matics can often be leveraged for use in computer net-
working and security. The challenges faced in bioinfor-
matics are quite similar to those in computer network
security. Several mechanisms have been devised in bio-
logical organisms to protect against pathogen invasion.
It is important to learn from these biological phenom-
ena and devise innovative solutions to protect com-
puter systems from software pathogens.
Virus detection systems prevalent today are based on
data analysis which looks for the presence of specific
patterns. The data may be composed of header infor-
mation in incoming packets at a firewall, data resident
on a node, or behavioral patterns of programs resident
on a computer. In most cases, the patterns of behavior
(signatures) are defined a priori based on knowledge of
existing pathogens. The signatures are usually gleaned
from virus code by teams of virus experts who dissect
;LOGIN: DECEMBER 2004 SECURITY: BIOLOGICAL MODELS FOR VIRUS PROPAGATION 49

50 ; L O G I N : VO L . 2 9 , N O. 6
the code and identify strings that uniquely identify the virus. The signature
database in virus detection programs becomes obsolete rapidly, as new virus
strains are released, and is updated as these new viruses are discovered. How-
ever, with the speed of virus propagation increasing—as is evident from the
spread of the Slammer worm, which infected more than 90% of vulnerable hosts
in 10 minutes—this mechanism is proving inadequate to control the spread of
viruses, with its consequent loss of data and services. It is imperative to develop
new virus detection software that does not rely solely on external intervention
but can detect new strains of viruses by organically generating “antibodies”
within a node. The physiology of cellular organisms contains several paradigms
that can be used as inspiration for developing such autonomous security sys-
tems in computer networks. Several streams of research on automatic detection
of virus (and worm) signatures are in progress (Kim and Karp, 2004), but this
research is still preliminary and not mature enough for commercial deployment.
One of the initial areas explored in the realm of biological models of computer
security involves the work of Forrest et al. (1994) with regard to virus detection.
Here the similarities are strikingly clear regarding the need to quickly and effi-
ciently identify viruses, generate “antibodies,” and remove them from the sys-
tem before they cause damage and propagate throughout the system. Prior to
this, Kauffman (1969) had been focused on understanding and modeling the
mechanics of gene transcription and translation within the cell. The concept of a
complex network of interactions describing gene regulation had been born in
the form of the Boolean network model. Now that the human genome has been
fully sequenced, the task of determining gene function is a significant focus.
However, specific genes identified in the sequence can interact with other genes
in complex ways. Some portions of the genome can turn off the expression of
other genes. These portions are called the structural and regulatory genes. Their
behavior is thought to be a defense against foreign sequences, perhaps passed on
from ancient viruses, from being expressed and potentially harming the organ-
ism (Hood, 2004). In fact, in this mechanism one can draw upon concepts that
apply directly to network security, namely, the idea of defensive code that can be
inherently activated to turn off dangerous code or viruses within the network.
One of the problems in virus protection systems is the result of false positives,
when portions of the code that provide legitimate functionality may be turned
off accidentally. The authors propose use of surrogate code that can replicate the
functionality of the pieces of code that are shut off, maintaining continuity in
the operations of the node. Specifically, fault-tolerant networks are capable of
surviving attacks and dynamically reconstituting services. Bush (2003) explores
the ability of a communication network to genetically constitute a service. The
network service evolves in real time using whatever building blocks are avail-
able within the network. Thus, a service damaged by a virus attack may be
genetically reconstituted in real time. The general concept was illustrated using
a specific example of a genetic jitter-control algorithm which evolved a 100-fold
decrease in jitter in real time.
Another biological paradigm which lends itself well to adaptation as a computer
security paradigm is protein pathway mapping. Living organisms have complex
metabolic pathways consisting of interactions between proteins and enzymes,
which may themselves have multiple subunits, alternate forms, and alternate
specificities. Molecular biologists have spent decades investigating these bio-
chemical pathways in organisms. These pathways usually relate to a known
physiological process or phenotype and together constitute protein networks.
These networks are very complex, with several alternate pathways through the
same start and end point. The partitioning of networks into pathways is, how-
ever, often arbitrary, with the start and finish points chosen based on “impor-
tant” or easily understood compounds. The models for biochemical pathways
that have been developed thus far primarily demonstrate the working of the cel-

lular machinery for specific tasks, such as metabolic flux and signaling. Several
different modeling techniques are used: (1) classical biochemical pathways (e.g.,
glycolysis, TCA cycle); (2) stoichiometric modeling (e.g., flux balance analysis);
and (3) kinetic modeling (e.g., CyberCell, E-Cell). More recently, cell metabo-
lism is being studied using cellular networks that are defined from large-scale
protein interaction and gene expression measurements.
Similar to the cellular networks in organisms, computer networks are complex
in nature and collectively exhibit complex behavior. In these networks, start and
end points can be arbitrarily chosen, and multiple paths may exist between the
same nodes. Protein networks are predetermined and stay fairly static, whereas
computer networks are constantly evolving with the addition of new nodes and
network links. In protein networks, interactions among proteins, enzymes, and
catalysts culminate in specific events. Analogously to protein networks, interac-
tions among nodes of computer networks result in specific events or conditions
in the network. The events may include propagation of viruses, denial-of-service
attacks, and congestion on the network. Investigation of the network pathways
along which the events propagate will enable us in forensic analysis to deter-
mine the root cause of the failures, as well as helping in developing intelligence
for prediction of network events.
One biological paradigm that is not directly related to the physiology of living
organisms is epidemiology that involves statistical analysis of disease propaga-
tion. Three basic models of disease propagation have been used extensively in
epidemiological studies. Kephart and White (1991) first used these epidemio-
logical models to study the spread of viruses on computer networks. Williamson
and Léveillé (2003) have also developed virus spread models in computer net-
works using the epidemiological metaphor. Since then, several researchers have
used variations of these basic models for studying the spread of computer
viruses on computer networks.
The authors (Goel and Bush, 2003) have used the biological paradigm of the
immune system, coupled with information theory, to create security models for
network security. Information theory allows generic metrics and signatures to be
created which transcend the specific details of a system or an individual piece of
code. They compare information-theoretic approaches with traditional string-
matching techniques. They also provide an analytic model that uses the epi-
demiological paradigm to study the behavior of the nodes. This article discusses
several different biological paradigms which inspire defense against pathogens
that invade computer networks, but it focuses on in-depth analysis of the
immune system model. Some of the other innovative biological models that are
currently being researched will be discussed in depth in a series of future arti-
cles.
Immune System Models
The role of the human immune system is to protect our body from pathogens
such as viruses, bacteria, and microbes. The immune system consists of various
kinds of cells, which operate autonomously and through interaction with each
other to create complex chains of events leading to the destruction of pathogens.
At a high level, cells can be categorized into two groups: detectors and effectors.
Detectors identify pathogens, and effectors neutralize them. There are two kinds
of immune responses evoked by the immune system: innate response and adap-
tive response. The innate immune response is the natural resistance of the body
to foreign antigens and is non-specific toward invaders in the body. During this
response, a specialized class of cells called phagocytes (macrophages and neu-
trophils) is used. These specialized cells, which have surface receptors that
match many common bacteria, have remained unchanged throughout evolu-

52 ; L O G I N : VO L . 2 9 , N O. 6
tion. This system reacts nearly instantaneously to detect pathogens in the body.
However, it is incapable of recognizing viruses and bacteria that mutate and
evolve.
The innate immune response is complemented by the adaptive immune
response, in which antibodies are generated to specific pathogens that are not
recognized by the phagocytes. The adaptive response system uses lymphocytes,
which have receptors for a specific strain instead of having receptors for multi-
ple strains as phagocytes do. Lymphocytes are produced in the bone marrow,
which generates variants of genes that encode the receptor molecules and
mature in the thymus. When an antigen is encountered, it is presented to the
lymphocytes in the lymphatic system. The lymphocytes that match proliferate
by cloning and subsequently differentiate into B-cells, which generate antibod-
ies, and T-cells, which destroy infected cells and activate other cells in the
immune system. Most effectors that proliferate to fight pathogens die; only
5–10% are converted into memory cells which retain the signature of the
pathogen that was matched. These memory cells permit a rapid response the
next time a similar pathogen is encountered, which is the principle used in vac-
cinations and inoculations. The number of memory cells produced is directly
related to the number of effector cells in the initial response to a disease. While
the total number of memory cells can become quite large, still, as an organism is
exposed to new pathogens, newer memory cells may take the place of older
memory cells, due to competition for space (Ahmed, 1998). This decrease in
memory cells leads to weakened immunity over time. Another reason for weak-
ened immunity is an immune response rate that is not sufficiently rapid to coun-
teract the spread of a powerful exotoxin, such as that produced by tetanus (Har-
court et al., 2004). Lymphocytes have a fixed lifetime, and if during this period
they do not match a pathogen, they automatically die.
The key to the functioning of the immune system is detection. Recognition is
based on pattern matching between complementary protein structures of the
antigen and the detector. The primary purpose of the genetic mechanism in the
thymus and bone marrow is to generate proteins with different physical struc-
tures. The immune system recognizes pathogens by matching the protein struc-
ture of the pathogen with that of the receptor. If the receptor of the antigen and
the detector fit together like a three-dimensional jigsaw puzzle, a match is
found. A fundamental problem with the detection mechanism of the immune
system is its computational complexity. For example, if there are 50 different
attributes with four different values, over six million different detectors are
required to cover the search space. The number of virus structures that can arise
due to different protein configurations is virtually infinite. In spite of high effi-
ciency in creating detectors and pattern matching at the molecular level, main-
taining a detector for each possible pathogen protein structure is not feasible.
The human immune mechanism solves this problem by using generalizations in
matching—that is, some features of the structure are ignored during matching.
This is called specificity of match; the more features are ignored, the lower the
specificity. The lower the specificity, the fewer the number of detectors required
for matching a population of pathogens and the more nonspecific is the
response. An explanation of specificity is elegantly described in J.H. Holland’s
description of classifier systems (1985). To cover the space of all possible non-
self proteins, the immune system uses detectors with low specificity. This
enables the immune system to detect most pathogens with only a few detectors;
however, it results in poor discrimination ability and a weak response to
pathogen intrusion. The immune system counters this problem by employing a
process called affinity maturation (Bradley and Tyrrell, 2000). Several methods
have been proposed for analytic representation of matching pathogen signatures
in the immune system, such as bit-strings (Farmer, Packard, and Perelson, 1986;
De Boer, Segel, and Perelson, 1992), Euclidean parameter spaces (Segel and

Perelson, 1988), polyhedron models (Weinand, 1991), and, more recently, Kol-
mogorov Complexity (Bush, 2002; Goel and Bush, 2003).
Several applications based on immune systems outside the area of biology have
recently emerged, the most notable of these being computer security. Kephart
(1995) was perhaps the first to introduce the idea of using biologically inspired
defenses against computer viruses and immune systems for computer security.
Forrest et al. (1994) also proposed the use of immune system concepts for
design of computer security systems and provided an elaborate description of
some immune system principles applicable to security. They presented three
alternate matching schemes—Hamming distance, edit distance, and r-contigu-
ous bits—arguing that the primary premise behind a computer immune system
should be the ability to distinguish between self and non-self. They presented a
signature scheme where a data tuple consisting of source IP address, destination
IP address, and a destination port number were used to distinguish self-packets
from non-self packets. Hofmeyr (1999) presented a detailed architecture of a
computer immune system. He analytically compared different schemes for
detection of pathogens, such as Hamming distance and specificity. There are sev-
eral other works in the literature on the use of immune systems for network
security, including Murray (1998), Kim and Bentley (1999), and Skormin et al.
(2001). Kephart and White (1991, 1993) present an architecture for an immune
system and the issues involved in its commercialization. They incorporate a
virus analysis center to which viruses are presented for analysis through an
active network. The Kolmogorov Complexity approach (Goel and Bush, 2003)
demonstrated a 32% decrease in the time required to detect a signature over two
common Hamming distance–based matching techniques, i.e., a sliding window
and the number of contiguous bit matches. The Kolmogorov Complexity–based
technique estimates the information distance of entire code sequences, not just
specific segments or bits. Using the entire code sequence makes it more difficult
to modify the virus so that it can hide in another portion of a legitimate code
segment.
Artificial immune systems consist of detectors and effectors that are able to rec-
ognize specific pathogen signatures and neutralize the pathogens. To detect
pathogens, the signature of incoming traffic packets is matched against signa-
tures of potential viruses stored in an immune system database. An immune sys-
tem that is capable of recognizing most pathogens requires a large number of
detectors. Low-specificity detectors that identify and match several viruses are
often used to reduce the number of detectors at the cost of increased false posi-
tives. The computational complexity of a computer immune system remains
fairly high, and individual nodes are incapable of garnering enough resources to
match against a large signature set. The computational complexity gets worse as
network traffic grows due to use of broadband networks, and it is straining the
capacities of conventional security tools such as packet-filtering firewalls. Mas-
sive parallelism and molecular-level pattern matching allow the biological
immune system to maintain a large number of detectors and efficiently match
pathogens. However, artificial immune systems have not achieved these levels of
efficiency. To reduce the computational burden on any individual node in the
network, all nodes need to pool their resources, share information, and collec-
tively defend the network. In addition, such inspection should be done within
the network itself, to improve efficiency and reduce the time required for react-
ing to an event in the network. This concept of collective defense enabled by a
unified framework is the primary premise of the authors’ research. To enable this
concept of collective network defense, they have proposed an approach based
on information theory principles using Kolmogorov Complexity measures.
To study the parameters and different schemes of detection and sampling in the
immune system, Goel et al. (working paper) have developed a simulation model
using RePast (Schaeffer et al., 2004), a simulation tool typically used for model-

54 ; L O G I N : VO L . 2 9 , N O. 6
ing self-organizing systems. The simulation models a classical immune system,
where new signatures are created by mutation of existing signatures which then
go through a maturation phase. The simulation also models a cooperative
immune system, where multiple nodes on the network share virus detection
information prevalent in the network to improve the efficiency of each immune
system. The research will investigate the trade-off between the additional bur-
den of sharing information across nodes and the benefit of improving scanning
efficiency by obtaining intelligence information on active or new pathogens.
Figures 1a and 1b show the impact of the match threshold and sampling rate,
respectively, on the performance of the immune system. Figure 1a shows a high
gradient between a threshold match of 0.2 and 0.4, which is the practical operat-
ing region for the immune system. Figure 1b shows an improved performance
with the sampling rate, which asymptotes around 70%.
Figure 1. Plots showing impact of match threshold and sampling rate on immune system-
metrics
Goel and Bush (2003) have also compared different signature metrics and have
demonstrated that Kolmogorov Complexity is a feasible metric for the signature
of pathogens.
Conclusion
The security models for detection and elimination of pathogens that invade
computer networks have been based on perimeter defense. Such defenses are
proving inept against fast-spreading viruses and worms. The current tools are
unable to guarantee adequate protection of data and unfettered access to serv-
ices. It is imperative to complement these existing security models with reactive
systems that are able to detect new strains of pathogens reliably and are able to
destroy them before they can cause damage and propagate further. Several bio-
logical paradigms provide a rich substrate to conceptualize and build computer
security models that are reactive in nature. Three specific mechanisms in mam-
malian organisms present the most potential: (1) the RNAi mechanism, (2) pro-
tein pathway mapping, and (3) the immune mechanism. In addition, the models

of disease control that study the spread and control of viruses suggest ways to
throttle the spread of viruses. Current work has mainly focused on the use of
immune and epidemiological models. It is time to move beyond these existing
models to other innovative models, such as those based on genomics and pro-
teomics. Such reactive models provide a scalable, resilient, and cost-effective
mechanism that may keep pace with constantly evolving security needs.
Acknowledgments
The authors are grateful to Damira Pon, from the School of Information Science
at the State University of New York at Albany, for a thorough review of this arti-
cle and her useful suggestions.
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