An Intrusion Detection System based on Deep Belief Networks

TL;DR

This paper presents a Deep Belief Network-based intrusion detection system that improves detection of underrepresented cyber-attacks using class balancing techniques and outperforms traditional models on the CICIDS2017 dataset.

Contribution

The paper introduces a DBN-based IDS that enhances detection of rare attacks and compares its performance with MLP and state-of-the-art methods.

Findings

DBN outperforms MLP in detecting underrepresented attacks

Class balancing improves detection performance

Proposed approach achieves competitive results on CICIDS2017

Abstract

The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect these attacks. The quality of the dataset used to train the DNN plays a critical role in the detection performance, with underrepresented samples causing poor performances. In this paper, we develop and evaluate the performance of DBN on detecting cyber-attacks within a network of connected devices. The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach. Several class balancing techniques were applied and evaluated. Lastly, we compare our approach against a conventional MLP model and the existing state-of-the-art. Our proposed DBN approach shows competitive and promising results, with significant performance improvement on the detection of attacks underrepresented in the training dataset.