Review of neural networks and particle swarm optimization contribution in intrusion detection

Maha Mahmood,Belal Al-Khateeb

doi:10.21533/pen.v7i3.622

Abstract

The progress in the field of computer networks and internet is increasing with tremendous volume in recent years. This raises important issues concerning security. Several solutions emerged in the past, which provide security at the host or network level. These traditional solutions like antivirus, firewall, spyware and authentication mechanism provide security to some extents but they still face the challenges of inherent system flaws and social engineering attacks. Some interesting solution emerged like intrusion detection and prevention systems but these too have some problems like detecting and responding in real time and discovering novel attacks. Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, an intrusion detection method based on neural network and Particle Swarm Optimization (PSO) algorithm is widely used in order to address the problem. This paper gives an insight into how PSO and its variants can be combined with various neural network techniques in order to be used for anomaly detection in network intrusion detection system in order to enhance the performance of intrusion detection system.

Highlights

There are some significant problems in the Intrusion Detection System (IDS), such as big load, slow detection speed and large amount of data
Neural networks are one of the most effective artificial intelligence methods that are employed in IDS [2]
The results showed that the accuracy for all models is increased when increasing the Artificial Neural Network (ANN) hidden neurons number

Summary

Introduction

There are some significant problems in the Intrusion Detection System (IDS), such as big load, slow detection speed and large amount of data. Network intrusion detection is a dynamic protection technology that is based on the self-defense of the web [1]. It can effectively deal with the external networks attacks and more importantly, it is able to prevent the violations from internal networks, this makes the intrusion detection technologies able to detect the known and unknown intrusions effectively and in good time speed. Artificial Neural Network (ANN) is used for IDS pattern analysis. Many irrelevant variables in the real intrusion detection sample data affect the ANN classification quality, decrease the real time capacity and increase many unwanted calculations for the intrusion detection. PSO is able to find a near to global optimal solution in a short time [7]

Neural Networks and Particle Swarm Optimization Algorithm

Network Intrusion Detection Model

Literature Review

Findings

Conclusions