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INTRUSION DETECTION SYSTEM USING RULE-BASED SYSTEMS

Presented By:
S. Jeya,

Assistant Professor, Department of Computer Applications (M. C. A), K.S.R. College of Engineering, Thiruchengode.

ABSTRACT


This paper describes a technique of applying Genetic Algorithm to network Intrusion Detection Systems. A brief overview of the Intrusion Detection System, genetic algorithm, rule based system and related detection techniques is presented. As the transmission of data over the internet increases, the need to protect connected systems also increases. Intrusion Detection Systems are the latest technology used for this purpose. Although the field of IDSs is still developing, the systems that do exist are still not complete, in the sense that they are not able to detect all types of intrusions. Some attacks which are detected by various tools available today cannot be detected by other products, depending on the types and methods that they are built on. Using a Genetic Algorithm is one of the methods that IDSs use to detect intrusions. They incorporate the concept of Darwin's theory and natural selection to detect intrusions. The focus of this paper is to introduce the application of GA, in order to improve the effectiveness of IDSs.

1. INTRODUCTION

In recent years, Intrusion Detection System has become one of the hottest research areas in Computer Security. It is an important detection technology and is used as a countermeasure to preserve data integrity and system availability during an intrusion. When an intruder attempts to break into an information system or performs an action not legally allowed, we refer to this activity as an intrusion. Intruders can be divided into two groups, external and internal. The former refers to those who do not have authorized access to the system and who attack by using various penetration techniques. The latter refers to those with access permission who wish to perform unauthorized activities. Intrusion techniques may include exploiting software bugs and system misconfigurations, password cracking, sniffing unsecured traffic, or exploiting the design flaw of specific protocols. An

Intrusion Detection System is a system for detecting intrusions and reporting them accurately to the proper authority.
There are two generally accepted categories of intrusion detection techniques: misuse detection and anomaly detection. Misuse detection refers to techniques that characterize known methods to penetrate a sys tem. These penetrations are characterized as a 'pattern' or a 'signature' that the IDS looks for. The pattern/signature might be a static string or a set sequence of actions. System responses are based on identified penetrations. Anomaly detection refers to techniques that define and characterize normal or acceptable behaviors of the system. Behaviors that deviate from the expected normal behavior are considered intrusions. IDSs can also be divided into two groups depending on where they look for intrusive behavior: Network-based IDS and Host-based IDS . The former refers to systems that identify intrusions by monitoring traffic through network devices. A host-based IDS monitors file and process activities related to a software environment associated with a specific host.
The architecture combines a number of different approaches to the IDS problem, and includes different AI techniques to help identify intrusive behavior. It uses both anomaly detection and misuse detection techniques and is both a network-based and host-based system. Genetic Algorithm has been used in different ways in IDSs. One network connection and its related behavior can be translated to represent a rule to judge whether or not a real-time connection is considered an intrusion. These rules can be modeled as chromosomes inside the population. The population evolves until the evaluation criteria are met. The generated rule set can be used as knowledge inside the IDS for judging whether the network connection and related behaviors are potential intrusions. The approaches described above, the IDS can be viewed as a rule-based system (RBS) and GA can be viewed as a tool to help generate knowledge for the RBS. This paper shows how network connection information can be modeled as chromosomes and how the parameters in genetic algorithm can be defined in this respect. Some examples are used to show the implementation.
2. MOTIVATION


One approach to computer security is to attempt to create a completely-secure system. Unfortunately, in many environments, it may not be feasible to render the computer system immune to intrusions, for several reasons. First, system software is becoming more complex. A major challenge programmer's face in software design is the difficulty in anticipating all conditions that may occur during program execution and understanding precisely the implications of even small deviations in such conditions. Thus, system software often contains flaws that may create security problems, and software upgrades often introduce new problems . Second, the increasing demand for network connectivity makes it difficult, if not impossible, to isolate and thereby protect a system from external penetration. Finally, a central component of computer systems, the computer network itself, may not be secure. This encouraged intrusion detection system researchers using genetic algorithm.

3. PROCESS MODEL FOR IDS

Many IDSs can be described in terms of three fundamental functional components:
? Information Sources - the different sources of event information used to determine whether an intrusion has taken place. These sources can be drawn from different levels of the system, with network, host, and application monitoring most common.
? Analysis - the part of intrusion detection systems that actually organizes and makes sense of the events derived from the information sources, deciding when those events indicate that intrusions are occurring or have already taken place. The most common analysis approaches are misuse detection and anomaly detection.
? Response - the set of actions that the system takes once it detects intrusions. These are typically grouped into active and passive measures, with active measures involving some automated intervention on the part of the system, and passive measures involving reporting IDS findings to humans, who are then expected to take action based on those reports.

3.1. Deployment strategy for IDS

Organizations should consider a staged employment of IDSs to allow personnel to gain experience and to ascertain how many monitoring and maintenance resources they will require. The resource requirements for each type of IDS vary widely, depending on the organization and systems environment. IDSs require significant preparation and ongoing human interaction. Organizations must have appropriate security policies, plans, and procedures in place so that personnel know how to handle the many and varied alarms IDSs produce. We recommend consideration of a combination of network-based IDSs and host based IDSs to protect an enterprise-wide network. We furthermore recommend a staged deployment, starting with network-based IDSs as they are usually the simplest to install and maintain. Next, protect critical servers with host-based IDSs. Utilize vulnerability analysis products on a regular schedule to test IDSs and other security mechanisms for proper function and configuration.
Honey pots and related technologies should be used conservatively and only by organizations with a highly skilled technical staff that are willing to experiment with leading-edge technology. Furthermore, such techniques should be used only after seeking guidance from legal counsel.
Protecting a full time Internet connected system is becoming more important than ever. An evaluation of needs should be conducted before selecting a product as concept, method and features vary. Firewalls act as a barrier between internal local networks and the outside world (Internet). It can keep the most unwanted characters out, but cannot necessarily tell what is going on inside the compound. Intrusion detection is considered by many to be the logical complement to network Firewalls, extending the security management capabilities of system administrators to include security audit, monitoring, attack recognition, and response. Intrusion detection systems are the equivalent of multi-sensor video monitoring and burglar alarm systems. They centralize this information, analyze it for patterns of suspicious behavior in much the same way a guard at a monitoring post watches the feeds from security cameras, and in some cases, deals with problems they detect. Most loss due to computer security incidents is still due to insider abuse. Intrusion detection systems, not Firewalls, are capable of detecting this category of security violation. To enhance security, an intrusion detection system can be run against the connection.
3.2. Strengths of IDS

Intrusion detection systems perform: Monitoring and analysis of system events and user behaviors Testing the security states of system configurations, base lining the

security state of a system, then tracking any changes to that Baseline, recognizing patterns of system events that correspond to known attacks, recognizing patterns of activity that statistically vary from normal activity, managing operating system audit and logging mechanisms and the data they generate, alerting appropriate staff by appropriate means when attacks are detected, measuring enforcement of security policies encoded in the analysis engine, providing default information security policies, allowing non-security experts to perform important security monitoring functions.
3.3. Limitations of IDS


Intrusion detection systems cannot perform: Compensating for weak or missing security mechanisms in the protection infrastructure. Such mechanisms include firewalls, identification and authentication, link encryption, access control mechanisms, and virus detection and eradication, Instantaneously detecting, reporting, and responding to an attack, when there is a heavy network or processing load, Detecting newly published attacks or variants of existing attacks, Effectively responding to attacks launched by sophisticated attackers, Automatically investigating attacks without human intervention, Resisting attacks that are intended to defeat or circumvent them, Compensating for problems with the fidelity of information sources, Dealing effectively with switched networks.

4. INTRODUCTION TO GENETIC ALGORITHM

chromosomes that function as basic instructions to the individual in a cause and effect manner. An individual is measured by the aggregate performance of its chromosomes.
An initial population is created by complete randomization of the chromosomes, and individuals of subsequent generations go through mutations, which are also randomized. As in Darwinism, a population that goes through many generations eliminates poor performing individuals and allows better performing individuals to replicate and mutate themselves during each generation. This genetic algorithm was designed so that each individual represented a possible behavioral model.

In this algorithm chromosomes means rules. Set of rules create population, a possible mathematical model known as individuals. The fitness is generally expressed within the algorithm as a floating point number with a predefined range of values, from best performing to worst performing.
The Algorithm is as follows:
? Randomly generate an initial population M(0) ? Compute and save the fitness u(m) for e ? Each individual m in the current population M(t) ? Define selection probabilities p(m) for each individual m in M(t) so that p(m) is proportional to
u(m)
? Generate M(t+1) by probabilistically selecting individuals from M(t) to produce offspring via genetic operators
? Repeat step 2 until satisfying solution is obtained.





computer simulation, a population of many individuals is created, each individual representing a possible mathematical model. Each individual has one or more
Figure 2. Simple Genetic Algorithm
In this study, the fitness of an individual was dependent upon how many attacks were correctly detected and how many normal use connections were classified as attacks. Correct detections were expressed as a positive ratio of total attacks while false positives were expressed as a negative ratio of total normal connections. The fitness function developed for this experiment, F, of specific individual d i was: where a is the number of correctly detected attacks,
A the number of total attacks, b the number of false positives, and B the certainty formula.
? ?
F( ?i) =
A B
total number of normal connections. The range of fitness values for this function was over the closed interval [-1,1] with -1 being the poorest possible fitness and 1 being the ideal. A high correct detection rate and a low false positive rate yielded a high score on the fitness function for an individual. Low detection rates or high false positive rates yielded low scores on the fitness function.
The model generated by this genetic algorithm was based on a new method of data analysis for the intrusion detection problem. Each node in the model's decision tree was designed to hold a randomized coefficient for the data, so that this coefficient multiplied by the data would yield a weight for the certainty of whether a certain record was an attack or not. The coefficients were based on an Ephemeral Random Constants (ERC) (Koza, 1992), random numbers generated by the genetic algorithm specific to mathematical modeling. These numbers' slight change in value was the basis for mutation in this genetic algorithm. For symbolic connection attributes (e.g., connection type), different weights were established for each symbol based on an ERC. For continuous connection attributes (e.g., bytes sent), ERC coefficients were randomly established for the data.
In continuous attributes that contained data of magnitudes apart, such as bytes sent, separate ERC coefficients were established for each magnitude of data. The certainty formula developed for this experiment, Ci, of whether record c was classified as an attack by model i was:
n
Ci(?) = ?(? i, j x ?j)
j = 1

where is the Ephemeral Random Constant-based coefficient for attribute c j and n is the number of attributes. An arbitrary threshold value was established, and any certainty values which exceeded this threshold value were classified as malicious attacks. The genetic algorithm was run for one hundred generations with one hundred individuals.
Forty-one different types of nodes were established, one for each of the forty-one connection record attributes. The genetic algorithm package ECJ 7 was used for this research (Luke, 2001). It provided the necessary population breeding, randomizing, and statistics gathering functions, from which this genetic algorithm was written. The genetic algorithm was written in Java, and the Webgain Visual Cafe 4.1 Expert Edition interface development environment was used to run the experiment. This experiment was run on a Dell computer with an Intel Pentium III 800 megahertz microprocessor and 256 megabytes of random access memory on Microsoft Windows 2000 using Sun Microsystem's Java Development Kit (JDK) version 1.3.1.
Information collected on each generation consisted of the mean fitness of all of the individuals within the generation, the fitness of the best performing individual, the correct detection rate and the false positive rate.
5. GENETIC ALGORITHM APPLIED TO INTRUSION DETECTION SYSTEM

Applying genetic algorithm to intrusion detection seems to be a promising area. We discuss the motivation and implementation details in this section.
5.1. Overview


Genetic algorithms can be used to evolve simple rules for network traffic. These rules are used to differentiate normal network connections from anomalous connections. These anomalous connections refer to events with probability of intrusions. The rules stored in the rule base are usually in the following form
if { condition } then { act}
For the problems we presented above, the condition usually refers to a match between current network connection and the rules in IDS, such as source and destination IP addresses and port numbers, duration of the connection, protocol used, etc., indicating the probability of an intrusion. The act field usually refers to an action defined by the security policies within an organization, such as reporting an alert to the system administrator, stopping the connection, logging a message into system audit files, or all of the above.
For example, a rule can be defined as:
If {the connection has following information: source IP
address 124.12.5.18; destination IP
address:130.18.206.55; destination port number: 21; connection time: 10.1 seconds } then {stop the connection}
The final goal of applying GA is to generate rules that match only the anomalous connections. These rules are tested on historical connections and are used to filter new
connections to find suspicious network traffic. In this implementation, the network traffic used for GA is a pre-classified data set that differentiates normal network connections from anomalous ones.
The genetic algorithm was run over a ten percent subset of the data, called the training data, and then tested over the entire data set to test real-world performance. In the real world, an empirical behavior model would rarely see any data which directly corresponds to training data.
5.1. Data Representation


In order to fully exploit the suspicious level, we need to examine all fields related with a specific network connection. For simplicity, we only consider some obvious attributes for each connection. Altogether there are fifty-seven genes in each chromosome. If the rule is able to find an anomalous behavior, a bonus will be given to the current chromosome. If the rule matches a normal connection, a penalty will be applied to the chromosome. Clearly no single rule can be used to separate all anomalous connections from normal connections.
The genetic algorithm starts with a population that has randomly selected rules. The population can evolve by using the crossover and mutations operators. Due to the effectiveness of the evaluation function, the succeeding populations are biased toward rules that match intrusive connections. Ultimately as the algorithm stops, rules are selected and added into the IDS rule base.

5.2. Parameters in Genetic Algorithm

There are many parameters to consider for the application of GA. Each of these parameters heavily influences the effectiveness of the genetic algorithm. We will discuss the methodology and related parameters in the following the evaluation function is one of the most important parameters in genetic algorithm. The proposed implementation differs from the scheme used by in that the definition on calculations of outcome and fitness is different. The following steps are used to calculate the evaluation function.
First the overall outcome is calculated based on whether a field of the connection matches the pre-classified data set, and then multiply the weight of that field. The Matched value is set to either 1 or 0.
Outcome = y\Matcheit*Weif>hll 7T
This scheme is straightforward and intuitive. Destination IP address is the target of an intrusion while the source IP address is the originator of the intrusion. These are the most important pieces of information needed to capture an intrusion. Destination port number indicates to applications that the target system is running. Some IP addresses are more probable targets for intrusions”for example, IP addresses for military domains. Domain-specific information is less important compared with the source IP addresses. Other parameters like duration, bytes sent by the originator, bytes sent by the receiver, and state are usually less important than the above fields but are still useful. The protocol and source port number fields are commonly dispensable and are used for identifying some specific intrusions.
The absolute difference between the outcome of the chromosome and the actual suspicious level is then computed using the following equation. The suspicious level is a threshold that indicates the extent to which two network connections are considered a "match." The actual value of suspicious level reflects observations from historical data.

? = Outcome - Suspicious level
Once a mismatch happens, the penalty value is computed using the absolute difference. The ranking in the equation indicates whether or not an intrusion is easy to identify.
Penalty = ? * ranking / 100
The fitness of a chromosome is computed using the above penalty:
fitness = 1 - penalty
Obviously, the range of the fitness value is between 0 and 1. By defining evaluation, we have incorporated both temporal and spatial information needed for identification of network intrusion.
5.3. Crossover and Mutation


Traditional genetic algorithms have been used to identify and converge populations of candidate hypotheses to a single global optimum. For this problem, a set of rules is needed as a basis for the IDS. As mentioned earlier, there is no way to clearly identity whether a network connection is normal or anomalous just using one rule. Multiple rules are needed to identify unrelated anomalies, which mean that several good rules are more effective than a single best rule. Another reason for finding multiple rules is that because there are so many network connection possibilities, a small set of rules will be far from enough.
The mutation operation should be meaningful during evolution. For example, each segment of the IP address should not exceed 255. Mutations should be done following the requirements specified in Table 1. These limitations can be enforced by defining proper mutation rules
5.5 Other Parameters

High
There are also other parameters that need to be considered, such as mutation rate, crossover rate, number of populations, and number of generations. These parameters should be adjusted according to the application environment of the system and the organization's security policy.
Destination IP Address
Source IP Address
Destination Port Number
Alternatively, some automated response, such as terminating that user's session, will be taken. Normally, a rule firing will result in additional assertions being added to the fact base. They, in turn, may lead to additional rule-fact bindings. This process continues until there are no more rules to be fired.
Consider the intrusion scenario in which two or more unsuccessful login attempts are made in a period of time shorter than it would take a human to type in the login information at a conventional keyboard. If the rule or rules of this scenario fire, then a specific user's suspicion level can be increased. The system may raise an alarm or freeze the named user's account. Account freeze would be entered into the fact database.
6. SYSTEM ARCHITECTURE


Duration

Bytes sent by originator
Bytes Sent by the receiver

Dataset

Network Sniffer

GA&
AI
Rule Set

Rule
Base

State
Low
Protocol
Source Port Number
Figure 3. Order of weights for fields in the evaluation function
6. RULE-BASED SYSTEMS

Figure 4. Architecture of applying GA into intrusion detection
We need to collect enough historical data that includes both normal and anomalous network connections. This is the first part inside the system architecture. The network sniffers analyze this data set and results are fed into GA for fitness evaluation. Then the GA is executed and the rule set is generated. These rules are stored in a database to be used by the IDS.



Expert systems detect intrusions by including intrusion scenarios as a set of rules. We can create more number of rules using genetic algorithm. These rules reflect the partially ordered sequence of actions that comprise the intrusion scenario. Some rules may be applicable to more than one intrusion scenario. The system state is represented in a knowledge base consisting of a fact base and a rule base. A fact base is a collection of assertions that can be made based on accumulated data from the audit records or directly from system activity monitoring. The rule base contains the rules that describe known intrusion scenario(s) or generic techniques. When a pattern of a rule's antecedent matches the asserted fact, a rule-fact binding is created. After this binding is made, if all the patterns of the rule have been matched, then a binding analysis is performed to make sure that all the associated variables with the rule are consistent with the binding. The rules with rule-fact bindings that meet the binding analysis requirements are then gathered into a set from which the "best" rule is picked, through a process called conflict resolution. The rule then fires. It may cause an alert to be raised for a system administrator.
7. CONCLUSION

In this paper, we discussed a methodology of applying genetic algorithm into network intrusion detection techniques. A brief overview of Intrusion Detection System (IDS), genetic algorithm, and related detection techniques are discussed. The system architecture is also introduced. Factors affecting the GA are addressed in detail. This implementation of genetic algorithm is unique as it considers both temporal and spatial information of network connections during the encoding of the problem; therefore, it should be more helpful for identification of network anomalous behavior.

REFERENCE

[1] Bezroukov, Nikolai. 19 July 2003. "Intrusion Detection (general issues)." Softpanorama: Open Source Software Educational Society. Nikolai Bezroukov.
[2] Bridges, Susan, and Rayford B. Vaughn. 2000. "Intrusion Detection Via Fuzzy Data Mining." In Proceedings of 12th
Annual Canadian Information Technology Security Symposium, pp. 109-122. Ottawa, Canada.
[3] Crosbie, Mark, and Gene Spafford. 1995. "Applying Genetic Programming to Intrusion Detection." In Proceedings of 1995 AAAI Fall Symposium on Genetic Programming pp. 1-8. Cambridge, Massachusetts. URL citeseer.nj.neccrosbie95applying.html (30 Oct. 2003).
[4] Graham, Robert. Mar. 21, 2000. "FAQ: Network Intrusion Detection Systems." RobertGraham.com Homepage.
Robert Graham. URL:
robertgrahampubs/network-intrusion-detection.html (30 Oct. 2003).
[5] Jones, Anita. K. and Robert. S. Sielken. 2000. "Computer System Intrusion Detection: A Survey." Technical Report. Department of Computer Science, University of Virginia, Charlottesville, Virginia. Li, Wei. 2002. "The integration of security sensors into the Intelligent Intrusion Detection System (IIDS) in a cluster environment." Master's Project Report. Department of Computer Science, Mississippi State University.
[6] McHugh, John, 2001. "Intrusion and Intrusion Detection." Technical Report. CERT Coordination Center, Software Engineering Institute, Carnegie Mellon University.
[13] Anomaly Detection in IP Networks by Marina Thottan and Chuanyi Ji IEEE Transactions on Signal Processing
Vol51 No8 August 2003.
[14] Twycross J., 2004, 'Immune Systems, Danger Theory and Intrusion Detection', to be presented at the AISB 2004 Symposium on Immune System and Cognition
(ImmCog-04) Leeds, U.K.
[15] P. D'haeseleer. An immunological approach to change detection: Theoretical results. In Proceedings of the 9th IEEE Computer Security Foundations Workshop, Los Alamitos, CA, 1996. IEEE Computer Society Press.
[16] P. D'haeseleer, S. Forrest, and P. Helman. An immunological approach to change detection: Algorithms, analysis and implications. In Proceedings of the 1996 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA, 1996. IEEE Computer Society Press.
[17] A Immunological Model of Distributed Detection and its Application to Computer Security. Steven A. Hofmeyr PhD thesis, Department of Computer Sciences.

AUTHOR PROFILE



[7] Miller, Brad. L. and Michael J. Shaw. 1996. "Genetic
Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization." In Proceedings of IEEE International Conf. on Evolutionary Computation, pp. 786¬791. Nagoya University, Japan.
[8] Paxson, Vern. 1998. "Bro: A System for Detecting Network Intruders in Real-time." In Proceedings of 7th USENIX Security Symposium, pp. 31-51. San Antonio, Texas.
[9] Pohlheim, Hartmut. 30 Oct. 2003. "Genetic and
Evolutionary Algorithms: Principles, Methods and Algorithms."
[10] Genetic and Evolutionary Algorithm Toolbox. Hartmut Pohlheim.URL:
geatbxdocu/algindex.html.
[10] Roesch, Martin. Nov. 7-12, 1999. "Snort - Lightweight Intrusion Detection for Networks." In Proceedings of13th
Systems Administration Conf. (LISA '99), pp. 229-238.Seattle, Washington.
[11] Sinclair, Chris, Lyn Pierce, and Sara Matzner. 1999. "An Application of Machine Learning to Network Intrusion Detection." In Proceedings of 1999 Annual Computer Security Applications Conf. (ACSAC), pp. 371-377. Phoenix,Arizona.URL:acsac1999/papers
/fri-b-1030-sinclair.pdf (30 Oct. 2003).
[12] Whitley, Darrell. 1994. "A Genetic Algorithm Tutorial." Statistics and Computing 4: 65-85.

S. Jeya
Assistant Professor,M.C.A. Dept.,K.S.R. College Of Engineering,Tiruchengode -
637 209,Tamil Nadu
Educational
details: B.Sc. Computer Science,
Sivanthi Adithanar College, Nagercoil,
M.C.A. Computer Applications, Sivanthi
Adithanar College,Nagercoil. M.Phil.
Computer Science.,
M.S.University,Tirunelveli. Ph.D.
Computer Science. Pursuing, Mother
Teresa Womens University, Kodaikanal.
Employement Details:1.Zyne
Technology, Bangalore, Software Engineer, 1 Yrs 6 Months,2. Rajaas Engineering College, Tirunelveli, Assistant Professor (Hod Mca),7 Yrs 2
Months, 3. K.S.R. College Of
Engineering, Tiruchengode - 9, Assistant Professor. Membership
Details: Life Member Of ISTE, Life
Member Of Oxford International
Journal.(201)
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This article is presented by:Wei Li
Department of Computer Science and Engineering
Mississippi State University, Mississippi State, MS 39762

Using Genetic Algorithm for Network Intrusion Detection

Abstract
This paper describes a technique of applying Genetic Algorithm (GA) to network Intrusion Detection Systems (IDSs). A brief overview of the Intrusion Detection System, genetic algorithm, and related detection techniques is presented. Parameters and evolution process for GA are discussed in detail. Unlike other implementations of the same problem, this implementation considers both temporal and spatial information of network connections in encoding the network connection information into rules in IDS. This is helpful for identification of complex anomalous behaviors. This work is focused on the TCP/IP network protocols.

Introduction
In recent years, Intrusion Detection System (IDS) has become one of the hottest research areas in Computer Security. It is an important detection technology and is used as a countermeasure to preserve data integrity and system availability during an intrusion. When an intruder attempts to break into an information system or performs an action not legally allowed, we refer to this activity as an intrusion (Graham, 2002; see also Jones and Sielken, 2000). Intruders can be divided into two groups, external and internal. The former refers to those who do not have authorized access to the system and who attack by using various penetration techniques. The latter refers to those with access permission who wish to perform unauthorized activities. Intrusion techniques may include exploiting software bugs and system misconfigurations, password cracking, sniffing unsecured traffic, or exploiting the design flaw of specific protocols (Graham, 2002). An Intrusion Detection System is a system for detecting intrusions and reporting them accurately to the proper authority. Intrusion Detection Systems are usually specific to the operating system that they operate in and are an important tool in the overall implementation an organization’s information security policy (Jones and Sielken, 2000), which reflects an organization's statement by defining the rules and practices to provide security, handle intrusions, and recover from damage caused by security breaches. There are two generally accepted categories of intrusion detection techniques: misuse detection and anomaly detection. Misuse detection refers to techniques that characterize known methods to penetrate a system. These penetrations are characterized as a ‘pattern’ or a ‘signature’ that the IDS looks for. The pattern/signature might be a static string or a set sequence of actions. System responses are based on identified penetrations. Anomaly detection refers to techniques that define and characterize normal or acceptable behaviors of the system (e.g., CPU usage, job execution time, system calls). Behaviors that deviate from the expected normal behavior are considered intrusions (Bezroukov, 2002; see also McHugh, 2001). IDSs can also be divided into two groups depending on where they look for intrusive behavior: Network-based IDS (NIDS) and Host-based IDS. The former refers to systems that identify intrusions by monitoring traffic through network devices (e.g. Network Interface Card, NIC). A host-based IDS monitors file and process activities related to a software environment associated with a specific host. Some host-based IDSs also listen to network traffic to identify attacks against a host (Bezroukov, 2002; see also McHugh, 2001). There are other emerging techniques. One example is known as a blocking IDS, which combines a host-based IDS with the ability to modify firewall rules (Miller and Shaw, 1996). Another is called a Honeypot, which appears to be a ‘target’ to an intruder, but is specifically designed to trap an intruder in order to trace down the intruder’s location and respond to attack (Bezroukov, 2002).

The Intelligent Intrusion Detection System (IIDS) is an ongoing project and implimentation at the Center for Computer Security Research (CCSR) in Mississippi State University. The architecture combines a number of different approaches to the IDS problem, and includes different AI techniques to help identify intrusive behavior (Bridges and Vaughn, 2001). It uses both anomaly detection and misuse detection techniques and is both a network-based and host-based system. Within the overall architecture of the IIDS, some open-source intrusion detection software tools are integrated for use as security sensors (Li, 2002), such as Bro (Paxson, 1998) and Snort (Roesch, 1999). Techniques proposed in this paper are part of the IIDS research efforts. Genetic Algorithm (GA) has been used in different ways in IDSs. The Applied Research Laboratories of the University of Texas at Austin (Sinclair, Pierce, and Matzner 1999) uses different machine learning techniques, such as finite state machine, decision tree, and GA, to generate artificial intelligence rules for IDS. One network connection and its related behavior can be translated to represent a rule to judge whether or not a real-time connection is considered an intrusion. These rules can be modeled as chromosomes inside the population. The population evolves until the evaluation criteria are met. The generated rule set can be used as knowledge inside the IDS for judging whether the network connection and related behaviors are potential intrusions (Sinclair, Pierce, and Matzner 1999). The COAST Laboratory in Purdue University (Crosbie and Spafford, 1995) implemented an IDS using autonomous agents (security sensors) and applied AI techniques to evolve genetic algorithms. Agents are modeled as chromosomes and an internal evaluator is used inside every agent (Crosbie and Spafford, 1995). In the approaches described above, the IDS can be viewed as a rule-based system (RBS) and GA can be viewed as a tool to help generate knowledge for the RBS. These approaches have some disadvantages. In order to detect intrusive behaviors for a local network, network connections should be used to define normal and anomalous behaviors. Sometimes an attack can be as simple as scanning for available ports in a server or a password-guessing scheme. But typically they are complex and are generated by automated tools that are freely available from the Internet. An example can be a Trojan horse or a backdoor that can run for a period of time, or can be initiated from different locations. In order to detect such intrusions, both temporal and spatial information of network traffic should be included in the rule set. The current GA applications do not address these issues extensively. This paper shows how network connection information can be modeled as chromosomes and how the parameters in genetic algorithm can be defined in this respect. Some examples are used to show the implementation. The rest of the paper is organized as follows. Section 2 provides a brief introduction to genetic algorithm. Section 3 describes the detailed implementation of applying genetic algorithm to intrusion detection. Section 4 discusses the architecture for the proposed implementation. Section 5 presents the conclusion and future work.


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