Fusion of ANN and SVM Classifiers for Network Attack Detection

TL;DR

This paper proposes a combined ANN and SVM classifier approach for detecting network attacks, demonstrating improved accuracy and visualization capabilities using the NSL-KDD dataset.

Contribution

It introduces a novel hybrid classifier method that integrates ANN and SVM for enhanced network attack detection and visualization.

Findings

Combined classifier improves detection accuracy.

Visualization of classification results is achieved.

Experimental results outperform individual classifiers.

Abstract

With the progressive increase of network application and electronic devices (computers, mobile phones, android, etc.) attack and intrusion, detection has become a very challenging task in cybercrime detection area. in this context, most of the existing approaches of attack detection rely mainly on a finite set of attacks. These solutions are vulnerable, that is, they fail in detecting some attacks when sources of informations are ambiguous or imperfect. However, few approaches started investigating in this direction. This paper investigates the role of machine learning approach (ANN, SVM) in detecting a TCP connection traffic as a normal or a suspicious one. But, using ANN and SVM is an expensive technique individually. In this paper, combining two classifiers are proposed, where artificial neural network (ANN) classifier and support vector machine (SVM) are both employed. Additionally, our proposed solution allows to visualize obtained classification results. Accuracy of the proposed solution has been compared with other classifier results. Experiments have been conducted with different network connections selected from NSL-KDD DARPA dataset. Empirical results show that combining ANN and SVM techniques for attack detection is a promising direction.