Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection

TL;DR

This paper introduces an autoencoder-based deep learning model for zero-day attack detection in intrusion detection systems, demonstrating high accuracy and improved recall over traditional methods on benchmark datasets.

Contribution

The paper presents a novel autoencoder approach for zero-day attack detection, outperforming existing outlier-based methods like One-Class SVM in accuracy and recall.

Findings

Autoencoders achieve 89-99% detection accuracy on NSL-KDD.

Autoencoders achieve 75-98% detection accuracy on CICIDS2017.

Trade-off observed between recall and fallout in detection performance.

Abstract

Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-day attacks. The aim is to build an IDS model with high recall while keeping the miss rate (false-negatives) to an acceptable minimum. Two well-known IDS datasets are used for evaluation-CICIDS2017 and NSL-KDD. In order to demonstrate the efficacy of our model, we compare its results against a One-Class Support Vector Machine (SVM). The manuscript highlights the performance of a One-Class SVM when zero-day attacks are distinctive from normal behaviour. The proposed model benefits greatly from autoencoders encoding-decoding capabilities. The results show that autoencoders are well-suited at detecting complex zero-day attacks. The results demonstrate a zero-day detection accuracy of [89-99%] for the NSL-KDD dataset and [75-98%] for the CICIDS2017 dataset. Finally, the paper outlines the observed trade-off between recall and fallout.