Pedro A. Diaz-Gomez
Ingenieria de Sistemas, Universidad El Bosque
Bogota, Colombia
Dean F. Hougen
Robotics, Evolution, Adaptation and Learning Laboratory (REAL Lab)
School of Computer Science, University of Oklahoma
Norman, OK, USA
Genetic Algorithms, Intrusion Detection, Off-Line Intrusion Detection, Misuse Detection.
One of the primary approaches to the increasingly important problem of computer security is the Intrusion
Detection System. Various architectures and approaches have been proposed including: Statistical, rule-based
approaches; Neural Networks; Immune Systems; Genetic Algorithms; and Genetic Programming. This paper
focuses on the development of an off-line Intrusion Detection System to analyze a Sun audit trail file. Off-line
intrusion detection can be accomplished by searching audit trail logs of user activities for matches to patterns
of events required for known attacks. Because such search is NP-complete, heuristic methods will need to
be employed as databases of events and attacks grow. Genetic Algorithms can provide appropriate heuristic
search methods. However, balancing the need to detect all possible attacks found in an audit trail with the need
to avoid false positives (warnings of attacks that do not exist) is a challenge, given the scalar fitness values
required by Genetic Algorithms. This study discusses a fitness function independent of variable parameters to
overcome this problem. This fitness function allows the IDS to significantly reduce both its false positive and
false negative rate. This paper also describes extending the system to account for the possibility that intrusions
are either mutually exclusive or not mutually exclusive.
The goal of a security system is to protect the most
valuable assets of an organization: data and informa-
tion. Different organizations will have very differ-
ent security policies and requirements depending on
their missions. This is the case for a bank, an Inter-
net Service Provider, a university, and a consulting
firm. However, all have data in some form, and their
security mechanisms are tasked with protecting the
privacy, integrity, and availability of the data. Many
efforts have been made to accomplish this goal: se-
curity policies, firewalls, Intrusion Detection Systems
(IDSs), anti-virus software, and standards to config-
ure services in operating systems and networks (Bace,
2000). This paper focuses on one of those topics: In-
trusion Detection Systems using the audit trail file.
The need for automated audit trail analysis was out-
lined a quarter century ago (Anderson, 1980) and it is
still present. Audit records are used and statistics are
Conducting research at the Robotics, Evolution, Adap-
tation and Learning Laboratory (REAL Lab), School of
Computer Science, University of Oklahoma.
gathered and matched against profiles. Information
in the matching profiles then determines what rules to
apply to update the profiles, check for abnormal activ-
ity, and report anomalies detected (Denning, 1986).
A key aspect of an IDS is the hypothesis that ex-
ploitation of a system’s vulnerabilities is based in the
abnormal use of the system (Denning, 1986). In one
form or another, all IDSs take into account this as-
sumption. Some, like IDES, NIDES, and EMER-
ALD (Bace, 2000), use statistics, while others use
a learning approach like Neural Networks (Bace,
2000), immune Systems (Forrest et al., 1994), Ge-
netic Algorithms (M
e, 1998), and Genetic Program-
ming (Crosbie and Spafford, 1995).
This paper presents a tool to perform misuse detec-
tion, using Sun audit trail files (Anonymous, 2000),
and uses the guidelines of a previously proposed
e, 1998) based on Genetic Algorithms (GAs).
To understand this, we first present the basics of com-
puter security (Section 2) and Genetic Algorithms
(Section 3). This is followed by a brief introduction
to the previous GA–based IDS (Section 4). Next, our
own improved IDS is covered thoroughly (Section 5),
followed by conclusions and future work (Section 6).
A. Diaz-Gomez P. and F. Hougen D. (2005).
In Proceedings of the Seventh Inter national Conference on Enterprise Information Systems, pages 66-73
DOI: 10.5220/0002553100660073
An Intrusion Detection System (IDS) is a system that
monitors and detects intrusions, or abnormal activi-
ties, in a computer or computer network. The IDS re-
ports corresponding alarms and may take immediate
action on the intrusions (Tjaden, 2004).
An intrusion is defined as an attempt to gain ac-
cess to a system by an unauthorized user. Misuse
refers to attempts to exploit weak points in the com-
puter or the abuse of existing system privileges. Ab-
normal activity means significant deviations from the
normal operation of the system or use of the system
by users (Tjaden, 2004).
Intrusion detection, then, is the process of monitor-
ing computer networks and systems for violations of
security policy. In the simplest terms, intrusion detec-
tion systems consist of three functional components:
1. an information source that provides a stream of
event records,
2. an analysis engine that finds signs of intrusions, and
3. a response component that generates reactions
based on the outcome of the analysis engine (Bace,
In order to get information for intrusion analysis, an
audit trail is often used. According with the Rainbow
Series of computer security documents, outlined by
the Department of Defense (Bace, 2000), the goals of
the audit mechanism are:
to allow the review of patterns of access,
to allow the discovery of both insider and outsider
attempts to bypass protection mechanisms,
to allow the discovery of a transaction of a user
from a lower to a higher privilege level,
to serve as a deterrent to users’ attempts to bypass
system-protection mechanisms, and
to serve as a yet another form of user assurance that
attempts to bypass the protection will be recorded
as discovered.
The need for automatic audit trail review to sup-
port security goals has been well documented with the
matrix in Table 1 suggested for classifying risks and
threats to computer systems (Anderson, 1980).
Table 1: Threat matrix. Redrawn with minor modifications
from Anderson, 1980
Not authorized to Authorized to
use data/program use data/program
Not authorized CASE A Blank
to use computer External Penetration
Authorized to CASE B CASE C
use computer Internal Penetration Misfeasance
This suggests a taxonomy for classifying risks and
threats to computer systems that differentiates be-
tween external and internal sources of problems. This
articulation has been useful in structuring require-
ments for audit trail content (Bace, 2000). Accord-
ing to this classification, this paper focuses on internal
penetration—an audit trail file of a authorized user is
analyzed in order to get misuse.
A formal definition of security says that it must
guaranty confidentiality, integrity, and availability.
Confidentiality refers to the fact that the information is
only known by authorized users. Integrity means that
the information is protected from alteration. Avail-
ability means that the system operates as it was de-
signed; it means, for example, that users have access
to it when they need it, where they need it, and in the
form they need it.
Another crucial aspect of any system’s security is
its security policy. A security policy is the set of
practices that is explicitly stated by an organization
in order to protect sensitive information (Crosbie and
Spafford, 1995).
The content of most security policies is driven by
a desire to address threats. A threat is defined as any
event that has the potential to harm a system. This
harm can be access of data by an unauthorized user,
destruction or modification of data, or denial of ser-
vice (Bace, 2000).
Security problems in computer systems result from
vulnerabilities. Vulnerabilities are weaknesses in sys-
tems that can be exploited in ways that violate secu-
rity policy. Although threat and vulnerability are in-
trinsically related, they are not the same. Threat is the
result of exploiting one or more vulnerabilities. Intru-
sion detection is designed to identify and respond to
both (Bace, 2000).
IDSs can be classified as host-based, multihost-
based, and network-based (Tjaden, 2004). Host-
based IDSs monitor a single computer using the audit
trail of the operating system whereas network-based
IDSs monitor computers on a network by scrutinizing
the audit trail of multiple hosts and network traffic.
A multihost-based IDS analyzes data from multi-
ple computers. Usually a module of the IDS runs
on each individual computer and sends reports to a
special module, sometimes called a director, running
on one machine. Since the director receives informa-
tion from the other computers, it can correlate this in-
formation to recognize intrusions that host-based sys-
tems would probably miss, such as worms. A host-
based IDS may not notice that type of intrusion. A
multihost-based IDS, with its data from a number of
different computers, would have a much better chance
of recognizing a worm as it spreads (Tjaden, 2004).
This paper deals with a host-based IDS, and an au-
dit trail file generated by a Sun machine is analyzed.
Genetic Algorithms (GAs) belong to the field of evo-
lutionary computation and have been widely used in
problems that require searching through a huge num-
ber of possibilities for solutions (Mitchell, 1998). The
strength behind GAs resides in the fact that the search
space is traversed in parallel by proposing solutions,
at the beginning randomly generated, and those solu-
tions are continuously evaluated with a fitness func-
Each set of solutions proposed is called a popula-
tion, and the first set is called the initial population.
Once all members in the initial population are evalu-
ated and assigned a fitness value, the selection mech-
anism is applied in order to produce the next genera-
The selection mechanism is applied in order to
choose parents that are going to reproduce. Often par-
ents are selected proportionally to their fitness value.
One common way to conceive of this is to imag-
ine mapping the fitness value of each individual to
a roulette wheel, where a greater fitness equates to a
larger space on the wheel. Once parents are chosen,
crossover and mutation are applied to them in order
to produce offspring.
Crossover exchanges subparts of parents (typically
two), while mutation changes randomly a particu-
lar part of an individual. The process of selection,
crossover, and mutation gives the next generation, and
the process is repeated until a solution is found or un-
til resources are exhausted.
One motivation for the topic of this research was the
paper GASSATA, A Genetic Algorithm as an Alter-
native Tool for Security Audit Trail Analysis” (M
1998), so let us analyze the theory behind it.
GASSATA is an off-line tool that increases security
audit trail analysis efficiency. The goals of this ap-
proach are the following:
to investigate misuse detection, i.e., to determine if
the events generated by a user correspond to known
attacks, and
to search in the audit trail file for the occurrence of
attacks by using a heuristic method, Genetic Al-
gorithms, because this search is an NP-complete
This approach is shown schematically in Figure 1.
The audit subsystem recognizes various kinds of
events (such as changing to a particular directory or
copying a file) which are recorded in the audit trail
file. The Syntax Analyzer classifies those audit events
Audit Trail
(W) (AE)
Figure 1: Prototype of GASSATA.
and generates the Observed Vector (OV ). The Ge-
netic Algorithm module finds the hypothesized vec-
tor I that maximizes the product W · I, subject to
the constraint (AE · I)
profiles where W is
a weight vector that reflects the priorities of the se-
curity manager, AE is the Attacks-Event matrix that
correlates sets of events with known attack profiles,
1 i N
, and N
is the number of known attack
profiles. The duration of the audit session was cho-
sen as 30 minutes and four (4) types of user were de-
fined: inexperienced, novice developer, professional,
and UNIX–intensive user.
The fitness function suggested in GASSATA is:
F (I) = α +
β T
. (1)
The goal of each component of the equation is as fol-
α: To maintain F(I) > 0, and therefore maintain
diversity in the population.
I: To allow for hypotheses of attacks. The system
is rewarded for hypothesizing attacks, particularly
those of greatest concern to the security manager.
This is generated randomly in the first generation.
β: To provide a slope for the penalty function.
T: To count the number of times (AE · I)
> OV
The system is penalized for hypothesizing sets of
attacks that could not have occurred, given the ob-
Experimental results for simulated attacks have
been reported (M
e, 1998).
As outlined before, intrusion detection systems con-
sist principally of three functional components:
1. an information source that provides a stream of
event records,
2. an analysis engine that finds signs of intrusions, and
3. a response component that generates reactions
based on the outcome of the analysis engine (Bace,
In order to get information for intrusion analysis,
an audit trail is often used. In this research we used
the Sun audit trail file (Anonymous, 2000).
5.1 The Security Audit Trail
Security auditing is the formal tracking and analysis
of actions taken by computers’ users. This process is
necessary in order to control current security policies
and detect anomalies, abuse, or misuse of the system
by users.
In order to provide individual user accountabil-
ity, the computing system identifies and authenticates
each user. Besides that, computers provide the pos-
sibility to register actions taken by users. Audit data
corresponds, then, to recorded actions taken by iden-
tifiable users, associated under the unique user iden-
tifier (ID). All processes and activity made by users
are recorded in the audit trail file. In a sense, audit
data is the complete recorded history of a users’ sys-
tem activity that can be gathered for an after-the-fact
investigation or to determine the effectiveness of ex-
isting security controls (Anonymous, 2000).
The audit trail data used in this research was down-
loaded from the MIT Lincoln Laboratory (Fried and
Zissman, 1998), and it has activity from various users.
e, 1998) works its input by user,
the file from was filtered by user.
The Solaris audit subsystem, called the Sun-
SHIELD Basic Security Module (BSM), is the mod-
ule responsible for the accountability of users’ ac-
tivity on the system. The file generated by the
BSM has records that describe user-level and kernel
events. Each record consists of tokens that identify
the process that performed the event, the objects on
which it was performed, and the objects’ attributes,
such as the owners or modes.
5.2 The Analysis Engine
Once the audit data is recorded, it must be reviewed
on a regular basis in order to maintain effective oper-
ational security. Administrators who review the audit
data must watch for events that may signify abnormal
use of the system. Some examples include:
trying to change sensitive information on records
of files requiring higher privilege,
killing critical processes,
trying to access different user’s files,
probing the system,
installing of unauthorized, potentially damaging
software, and
exploiting a security vulnerability to gain higher or
different privileges.
In order to provide system administrators with the
ability to effectively audit user actions, the software
developed, as suggested for GASSATA (M
e, 1998),
provides the capability to read the audit trail and per-
form analysis of those records based on known intru-
sions. The program developed to simulate GASSATA
consists of two parts: the first one, the scanner, reads
the audit trail to classify and count the user’s events,
and the second one, the analysis engine, is a Genetic
Algorithm. In schematic form, the software devel-
oped has the same architecture shown in Figure 1.
5.2.1 Data Structures
The scanner looks at all the audit trail records for a
user and counts the different kind of events. The out-
put of this program is an array of size N
called the
Observed Vector (OV ). Each position of this array
corresponds to the number of occurrences of an event
according with the classification stated before.
The Genetic Algorithm receives as input the Ob-
served Vector and the Attack-Events matrix (AE) of
known attacks (M
e, 1998). In Table 2, each row corre-
sponds to an event type. Each numeric column—i.e.,
0 through 23—corresponds to a known attack; col-
umn I corresponds to a hypothesis that is being eval-
uated, column AE I corresponds to the multiplica-
tion of matrix AE with hypothesis vector I, column
OV corresponds to the actual number of events that
occurred—this is the output of the scanner program,
and column entry i in T is set to 1 if (AE ·I)
> OV
i.e., if the number of hypothesized events of type i are
greater than the actual number of events of that type
registered in the audit trail file. Column T
the number of of hypothesized attacks that require
more events of type i than actually occurred (see Sec-
tion 5.2.3).
The Observed Vector contains the number of sys-
tem events performed by a user in a session. As
shown in Table 3, the first row is the type of event
and the second row is the number of events of that
The first position of row 2 shows 1, which means
that there was a 1 event of type User
login fail,
the eighth position shows 40, which means that there
were 40 events of type ls fail. Other event types
are listed elsewhere (M
e, 1998).
5.2.2 The Genetic Algorithm
The analysis engine is a Genetic Algorithm that works
as follows:
Generation of the First Population
Table 2: The Attack Events matrix AE, an example hypothesis Vector I, the resulting Multiplication Vector MV (= AE I),
an example Observed Vector OV , and the count of overestimates in columns T and T
Selection of Parents
Reproduction with crossover and mutation
Until an acceptable Solution is found or
resources are exhausted
An individual (I) in the population is an array of 24
positions. Each position corresponds to an attack that
is being hypothesized.
The First Population is generated randomly. The
algorithm is guessing the possible occurrence of at-
tacks. 40 individuals are generated.
Selection of Parents. Parents are selected according
to their fitness value. The fitness function guides the
algorithm to find the possible intrusions.
Crossover and Mutation. Parents that are chosen
are crossed with a probability equal to 60% and mu-
tated with a probability of 2.4%. Mitchell (1998) sug-
gests values like 70% for crossover and 0.1% for mu-
tation as commonly appropriate.
Number of generations. The number of genera-
tions for each test was variable. At the very be-
ginning of our experiments, for example, we keep
track of the fitness values and stop after 500,000
generations; we found, then, the greatest fitness
value of all the generations and used it with a
threshold equal to STDV(Max
fitness value)/4,
so the algorithm begins to run, and if the fit-
ness value goes less than Max fitness v alue)/4
fitness value)/4 the algorithm stops.
The Attack-Event matrix, which gives the number
of events by attack, is multiplied by the individual
I that hypothesized the occurrence of attacks. The
result is the Multiplication Vector (MV) that gives
the classified total number of events hypothesized
(= AE I). This vector is evaluated using the Ob-
served Vector that records the number of events that
have occurred. Each position of the result vector MV
is compared with the corresponding position in OV .
If M V
, the hypothesized number of events of type
i, is less than OV
(the audit trail observed number
of events of type i), then the hypothesis is possible.
Table 3: An example of an Observed Vector
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
1 12 6 0 4 9 11 40 34 9 5 45 0 0 2 7 0 29 0 0 0 0 0 0 0 0 0 0
Table 4: An example of an Individual. Positions 4, 5, and 8 that have 1 are showing the possible occurrence of attacks 4, 5,
and 8
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
But if, on the contrary, M V
is greater than OV
, that
means that the algorithm hypothesized more intru-
sions than actually occurred, a situation that must be
take into account using a penalty factor in the fitness
function. The penalty term is then related with the
number of events in MV
that are greater than the ob-
served events in OV
5.2.3 An Improved Fitness Function
A genetic algorithm needs a fitness function that
combines objectives and constraints into a single
value (Coello, 1998). The problem is not only to find
the appropriate function but also to provide accurate
values to the parameters that produce the correct so-
lution to the problem for as many instances as pos-
sible. It appears that the fitness function proposed
in GASSATA, a combination of objectives and con-
straints into a single value using arithmetical opera-
tions, should be correct. However, it is difficult to set
the parameters so that the algorithm finds intrusions
and converges.
For this problem GASSATA uses a fitness function
that has principally two terms:
that is
rewarding, and β T
that is penalizing.
As the fitness function is giving a payoff to the
highest valued individual, the term
guiding the solution to have the maximum number of
intrusions. However, this is good enough until the
correct set of intrusions are found. Later on, i.e., if
more intrusions than that are hypothesized, the prob-
lem of false positives occurs. On the other hand, the
term β T
is diminishing the fitness but in doing
so various intrusions can hit the same event. When
this happens the counting of overestimates is wrong.
These two facts were tested and false positives and
false negatives were found. (Results of this testing
and an analysis of the reason for it will be published
elsewhere (Diaz-Gomez and Hougen, 2005). For the
present work, we concentrate on improvements to the
The solution proposed has two parts:
1. remove the positive term
, and
2. count overestimates in the correct way; this means,
if two intrusions require the same event in numbers
in excess of the number of actual events then count
them both, and so forth. Call this T
With this in mind, the fitness function only has one
term, the penalty function and the new fitness function
suggested is
F (I) = N
where N
corresponds to the total number of classi-
fied events. For testing this value is 28. T
sponds to the number of overestimates, i.e., the num-
ber of times (AE · I)
> OV
It must be taken into account that the role of α cor-
responds now to N
and that β is equal to one. How-
ever, the term
was suppressed, as stated be-
fore. It must be reinforced that the reason for doing
that is because the term
is giving the number
of intrusions hypothesized but those intrusions have
not been evaluated yet. In doing the evaluations, that
may produce an incorrect count of overestimates.
Now, the hypothesized vector I is really evaluated
in T
; the better the hypothesized vector, the smaller
is, and of course, F (I) N
, the maximum. The
fitness function is evaluating only the T
term. There
is a maximum when T = 0.
In this study we divided the set of intrusions into
two subsets: mutually exclusive and not mutually ex-
clusive intrusions. We define mutually exclusive in-
trusions those that can not occur at the same time, de-
pending of the Observed Vector in the analysis, i.e.,
if in considering each intrusion alone T = 0 but in
considering them at the same time T 6= 0. That is the
case for all intrusions that share some event type; for
example, intrusions number 5, 19 and 21, in each of
which event number 6 is present (see Table 2).
As our genetic algorithm runs, it creates aggre-
gate solution sets of all possible compatible intrusions
found. To do this, it records all the realistic solutions
(those where T = 0) found in the search space and
keeps track of each intrusion it finds within each real-
istic solution. The algorithm then checks if the intru-
sion already exists in its current solution set and, if it
does not, then it checks if it is mutually exclusive or
not in order to add it to the corresponding aggregate
solution set. In this way, the algorithm builds up sets
of all compatible solutions. This mechanism can be
seen as a replacement for the positive term
which was misleading the genetic algorithm in GAS-
The results found with this fitness function are
shown in Table 5. The data corresponding to users
2051 and 2506 was extracted by our scanner from au-
dit data files downloaded from the Lincoln Laboratory
at MIT (Fried and Zissman, 1998).
Table 5: Results with fitness function F (I) = N
averaged over 10 runs
Average Count Average %
User False + False - Detected False + False - Detected
2051_7 0 0 3 0 0 100
2051_11 0 0 4 0 0 100
2506_15 0 0 4 0 0 100
Zero Vector 0 0 0 0 0 100
0 0.1 0.9 0 10 90
0 0 2 0 0 100
0 0 3 0 0 100
One Intrus.
Two Intrus.
Three Intrus.
As can be seen, with the fitness function proposed
there are no false positives and the number of false
negatives decreases dramatically. This time 70 runs
were performed with different data and only one time
a false negative was present.
These results are significantly better than any of
those found with any of the parameters we tested for
the original GASSATA fitness function. For example,
setting α = N
> N
/2, and β = 1
resulted in the performance shown in Table 6.
Table 6: Results with Fitness Function as in Equation 1 us-
ing α = N
> N
/2, and β = 1
5.3 Improvements
As with many heuristic tools, we have difficulties in
the implementation of GASSATA. Some difficulties
arise in providing accurate values for the fitness pa-
rameters α, W , and β. In doing that we found a high
percentage of false positives, so this study made the
following improvements in order to:
dismiss false positives and false negatives,
find the maximum set of intrusions and disaggre-
gate them as mutually exclusive or not,
record all events not considered in the intrusion
As was explained in Section 5.2.3 the term
proposed for the fitness function in
Equation 1 is guiding the algorithm to give false pos-
itives, so with the new fitness function proposed in
this research (see Equation 2), we have discarded
that term. Experimentally, as is shown in Table 5,
there were no false positives with our fitness function
F (I) = N
T .
We also managed to all but eliminate false neg-
atives by building up aggregate solution sets of all
compatible intrusions found, as also explained in Sec-
tion 5.2.3.
Another improvement is related with the recording
of events not considered in the analysis. That is, the
scanner is looking for predefined events; if there are
events not defined, those are recorded, so the system
administrator can check them and evaluate if there are
critical events not considered and that can be included
in the scanner.
This paper proposes a fitness function independent
of variable parameters, making the fitness function
to solve this particular problem quite general and in-
dependent of the audit trail data. This approach can
be generalized to similar multi-objective fitness func-
tions for genetic algorithms. At the same time the
system proposed improves the one suggested previ-
ously (M
e, 1998) by recording all the events not con-
sidered in the intrusion analysis, finding the maxi-
mum set of intrusions and disaggregating them as mu-
tually exclusive or not, and diminishing the false pos-
itives and false negatives.
One topic that can be addressed in future work
is to investigate system performance using different
crossover and mutation rates.
We also consider it important for the future of intru-
sion detection systems to consider the standardization
of audit trail files. Such a standard has the principal
benefit that it would enable logs generated by differ-
ent operating systems to be reconciled. With the au-
dit trail standardized, the analysis of logs by a central
engine would be simpler, because that engine would
deal with only one file structure.
Another improvement of the specific intrusion sys-
tem developed is the use of real-time intrusion detec-
tion, because such a system can catch a range of intru-
sions like viruses, Trojan horses, and masquerading
before these attacks have time to do extensive dam-
age to a system.
This paper presented some of this new research in
intrusion detection, by using a GA as an analytical
engine that performs intrusion detection. However,
the field of IDSs is quite diverse and other approaches
such as immune systems and neural networks have
been developed in order to improve this mechanism.
The field is deep and there are promising new ways
to think about it. Evolutionary computation offers
a chance to see intrusion detection systems with the
ability to evolve—evolution that could sometimes ex-
ceed human programmers. There are new paradigms
to explore and we can use computers themselves as
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