Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

TL;DR

This paper analyzes various intelligent classifiers for intrusion detection, evaluates their accuracy using the NSL-KDD dataset, and develops a Java-based system with hybrid classifiers to improve detection performance.

Contribution

It introduces a hybrid AI classifier approach for intrusion detection and compares its effectiveness with other classifiers using a real-world dataset.

Findings

Hybrid classifiers improve detection accuracy over single classifiers

Java implementation demonstrates practical applicability

Experimental results show enhanced efficiency in intrusion detection

Abstract

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.