Comparison of Selection Methods and Crossover Operations using ...
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Comparison of Selection Methods and Crossover Operations using ...
System can be a piece of installed software or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal ...
Comparison of Selection Methods and Crossover Operations using Steady State Genetic Based Intrusion Detection System 1
Firas Alabsi,2 Reyadh Naoum 1, 2
Middle East University
ABSTRACT Intrusion Detection Systems are systems built to detect the unwanted attacks. Genetic Algorithm is a method that mimics the process of natural evolution; it was used to support the Intrusion Detection Systems. Genetic Algorithm contains several elements such as population size, evaluation, encoding, crossover, mutation, replacement and stopping criterion. Elements specifications must be determined before using Genetic Algorithm. The performance of Genetic Algorithm depends mainly on these specifications. The aim of this paper is to compare different types of genetic operators and monitor their performance in Intrusion Detection System, to determine the Selection type and Crossover type to be worked together and perform better. Keywords: Steady State Genetic Algorithm, Intrusion Detection System, Selection, Crossover.
1.
INTRODUCTION
"Intrusion detection is the act of detecting unwanted traffic on a network or a device. An Intrusion Detection System can be a piece of installed software or a physical appliance that monitors network traffic in order to detect unwanted activity and events such as illegal and malicious traffic" [1]. The strength of the detection system depends mainly on the rules stored within the system. Each rule consists of an action part and a condition part. The condition contains the features values whereas the action determines the attack type as (U2R, R2L, Dos or Probe). In order to determine if the traffic is wanted or unwanted, the system contains a pool of rules that represents the unwanted traffic. The received traffic will be compared with the condition part of each rule stored in the rules pool. If the traffic matches one of the rules which stored in the rules pool, then the traffic is detected as unwanted. In many cases the rules pool does not have a sufficient number of rules. To solve this problem, Steady State Genetic Algorithm can be used to create new rules and discover the hidden rules which make the rules pool sufficient to detect the unwanted traffic. "Steady State Genetic Algorithm (SSGA) is used to give a chance for previous rules from previous generation to participate in detecting intrusions in the next generations" [3].
2. STEADY STATE GENETIC ALGORITHM (SSGA) ELEMENTS 2.1
Population
Cleary [4] described the population as a result of a single iteration of genetic algorithm. Iteration can create a new population. Population contains a set of chromosomes;
each chromosome is one complete possible solution to the problem to be solved using genetic algorithm.
2.2
Evaluation
For each chromosome there is a fitness function used to evaluate the fitness of each chromosome. Fitness's value reflects the quality of each chromosome.
2.3
Encoding
The gene is a problem parameter; it can be encoded as a binary, integer, or float number.
2.4
Selection
It is the process of selecting the chromosomes to apply Steady State Genetic Algorithm.
TYPES OF SELECTION ARE: a.
Roulette Wheel Selection (RWS):
The chance of a chromosome being selected is proportional to its fitness value. This can be worked as in the following steps: Step 1: Find the fitness value (fv) for each chromosome in the population using Fitness Function. Step 2: Calculate sum fitness (Sf) for all the chromosomes in the population:
