Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Nadia F Al Bakri,Ahmed T Sadiq,Atheer R Muhsen,Ghazwh G Jumaa

doi:10.3991/ijim.v15i16.24173

Abstract

The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD &amp; UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.

Highlights

Computer network attacks are several bad things so that information and services within computer networks are damaged, denied, and degenerated or destructed
This paper presents a review of comparing between machine learning classification methods applied to analysis NSL-KDD dataset and UNSW-NB15 dataset using some algorithms for feature selection to decrease the dimensionality of the datasets, using the same classification methods and compare the results of different feature selection methods
This paper presents an approach as a features selection algorithm based on Meerkat Clan Algorithm (MCA), the proposed algorithm is a wrapper feature selection type

Summary

Introduction

Computer network attacks are several bad things so that information and services within computer networks are damaged, denied, and degenerated or destructed. Intrusion Detection Network (NID) is an intrusion detection mechanism that attempts to detect unauthorized access to a computer network for signs of malicious activity through analysis of network traffic. In this huge area of Network Intrusion Detection (NID), several fields of study exist. Many binary meta-heuristic algorithms are used to approximate the optimal solution by removing irrelevant features within a suitable computational time. Meta-heuristic algorithms are known as natural-based algorithms that are more appealing than conventional approaches for resolving optimization problems [4,5,6] They function without derivatives and are suitable for high-dimensional space problems. Feature selection offers many advantages some of them are illustrated below: [12,13]

Methods

Results

Conclusion