An Intrusion Detection System Based on Hybrid of Particle Swarm Optimization (PSO) and Magnetic Optimization Algorithm (MOA)

EasyChair Preprint 5337

Abstract

Nowadays, the number of attacker increasing rapidly due to the modern technologies. Most companies or an organization use Intrusion Detection System (IDS) to secure their network system. Many researchers proposed various ways to improve the IDS such using optimization techniques. Artificial Intelligence (AI) techniques also proposed in IDS to achieved high accuracy of detection for example; artificial neural network, particle swarm optimization and genetic algorithm. Artificial neural network (ANN) and Particle Swarm Optimization (PSO) used in this paper to compare the method and performances in IDS environment. The ANN output value will be compared with the result where ANN supported by PSO to produce more accurate value. KDD CUP ’99 Dataset used as the benchmark of IDS and will be simulated in MATLAB Simulink 2013. 200 datasets used consists of attacks and normal activities as an input. In this paper, Smurf and Neptune attacks selected for detection of attack category.

Keyphrases: Artificial Neural Network (ANN), Intrusion Detection System (IDS), KDD ’99 Dataset, Particle swam optimization (PSO)

@booklet{EasyChair:5337,
  author    = {Siti Norwahidayah Wahab and Noor Suhana Sulaiman and Noraniah Abdul Aziz and Nur Liyana Zakaria and Nurul Farahah Abdul Halim and Ainal Amirah Abdul Aziz},
  title     = {An Intrusion Detection System Based on Hybrid of Particle Swarm Optimization (PSO) and Magnetic Optimization Algorithm (MOA)},
  howpublished = {EasyChair Preprint 5337},
  year      = {EasyChair, 2021}}