Investigating Application of Deep Neural Networks in Intrusion Detection System Design

TL;DR

This paper explores the application of Deep Neural Networks, specifically Multilayer Perceptron, for intrusion detection, but finds that the model struggles to accurately classify network intrusions despite feature selection efforts.

Contribution

It investigates the effectiveness of DNNs in intrusion detection and applies feature selection to improve model efficiency, highlighting current limitations.

Findings

Multilayer Perceptron failed to accurately classify intrusions.

Feature selection reduced feature set significantly.

Model showed limited success in distinguishing attack types.

Abstract

Despite decades of development, existing IDSs still face challenges in improving detection accuracy, evasion, and detection of unknown attacks. To solve these problems, many researchers have focused on designing and developing IDSs that use Deep Neural Networks (DNN) which provides advanced methods of threat investigation and detection. Given this reason, the motivation of this research then, is to learn how effective applications of Deep Neural Networks (DNN) can accurately detect and identify malicious network intrusion, while advancing the frontiers of their optimal potential use in network intrusion detection. Using the ASNM-TUN dataset, the study used a Multilayer Perceptron modeling approach in Deep Neural Network to identify network intrusions, in addition to distinguishing them in terms of legitimate network traffic, direct network attacks, and obfuscated network attacks. To further enhance the speed and efficiency of this DNN solution, a thorough feature selection technique called Forward Feature Selection (FFS), which resulted in a significant reduction in the feature subset, was implemented. Using the Multilayer Perceptron model, test results demonstrate no support for the model to accurately and correctly distinguish the classification of network intrusion.