Utilising deep learning techniques for effective zero-day attack detection

Hanan Hindy, Robert Atkinson, Christos Tachtatzis, Jean-Noël Colin, Ethan Bayne, Xavier Bellekens

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
9 Downloads (Pure)


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.

Original languageEnglish
Article number1684
Number of pages16
Issue number10
Publication statusPublished - 14 Oct 2020


  • autoencoder
  • artificial neural network
  • one-class support vector machine
  • intrusion detection
  • zero-day attacks
  • CICIDS2017


Dive into the research topics of 'Utilising deep learning techniques for effective zero-day attack detection'. Together they form a unique fingerprint.

Cite this