Implementation of intrusion detection system and improvement utilising genetic algorithm
by Ke Huang; Bichuan Sun; Xianming Sun; Mohammad Shabaz; Rijwan Khan
International Journal of Nanotechnology (IJNT), Vol. 20, No. 5/6/7/8/9/10, 2023

Abstract: The rapid evolution of computer technology due to the vast services and applications has made people dependent on computer technology as a result there could prevailing threats which need to be addressed, while dealing with the networking background. Therefore we require the security to assist the networking technology in revealing the vulnerability to abuses against the uses of a computer or its application. In today's interacting environment intrusion detection system (IDS) is one of the major security components. That uses the security tools to be utilised in the traditional way like IDS, fire-walls have also become a significant method. The IDS is a security system that provides the effective methods for computer network safety. In this paper we are addressing the detection rate maximisation and false rate minimisation that is a major problem due to its inability to discover a particular attack. This problem is addressed by the genetic algorithm (GA) approach that is presented in this paper using the fuzzy methods for the IDS development. As a robust technology, it is most commonly utilised for IDS design and is based on machine learning. It is a search algorithm based on natural selection and genetics principles. For the GA specific problem solution, the fittest survival principle is utilised by GA functions for better approximation generation. In our approach, two datasets are utilised to perform the experiments. In the first dataset, 137 attacks and 840 normal connections total 977 connections in kept in dataset 1 and in dataset-2, 234 attacks and 744 normal connections, totally 978 connections are included. For both experiments, the presented technique manages high detection rate, high accuracy, and the low false alarm. Therefore proposed technique outperforms the existing techniques with 96.57% detection rate and 3.12% false alarm.

Online publication date: Tue, 10-Oct-2023

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