Classification of artificial intelligence ids for smurf attack

TL;DR

This paper reviews AI-based intrusion detection systems, emphasizing the importance of normalization, and proposes a support vector machine approach for detecting Smurf attacks with improved accuracy.

Contribution

It introduces a classification framework for AI-based IDS techniques and proposes a support vector machine method specifically for Smurf attack detection.

Findings

Support vector machine achieved high accuracy in detecting Smurf attacks.

Normalization enhances the effectiveness of AI techniques in IDS.

The proposed method outperforms traditional signature-based IDS.

Abstract

Many methods have been developed to secure the network infrastructure and communication over the Internet. Intrusion detection is a relatively new addition to such techniques. Intrusion detection systems (IDS) are used to find out if someone has intrusion into or is trying to get it the network. One big problem is amount of Intrusion which is increasing day by day. We need to know about network attack information using IDS, then analysing the effect. Due to the nature of IDSs which are solely signature based, every new intrusion cannot be detected; so it is important to introduce artificial intelligence (AI) methods / techniques in IDS. Introduction of AI necessitates the importance of normalization in intrusions. This work is focused on classification of AI based IDS techniques which will help better design intrusion detection systems in the future. We have also proposed a support vector machine for IDS to detect Smurf attack with much reliable accuracy.