An Implementation of Intrusion Detection System Using Genetic Algorithm

TL;DR

This paper presents an intrusion detection system that employs genetic algorithms to improve detection efficiency and reduce complexity, validated using the KDD99 dataset.

Contribution

It introduces a novel IDS approach using genetic algorithms with detailed parameter tuning and implementation, enhancing detection capabilities.

Findings

Achieved reasonable detection rate on KDD99 dataset

Utilized genetic algorithms to filter traffic data effectively

Reduced complexity of intrusion detection process

Abstract

Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.