review-article

A review of Machine Learning-based zero-day attack detection: : Challenges and future directions

Author:

Yang GuoAuthors Info & Claims

Volume 198, Issue C

Pages 175 - 185

https://doi.org/10.1016/j.comcom.2022.11.001

Published: 15 January 2023 Publication History

Abstract

Zero-day attacks exploit unknown vulnerabilities so as to avoid being detected by cybersecurity detection tools. The studies (Bilge and Dumitraş, 2012, Google, 0000, Ponemon Sullivan Privacy Report, 2020) show that zero-day attacks are wide spread and are one of the major threats to computer security. The traditional signature-based detection method is not effective in detecting zero-day attacks as the signatures of zero-day attacks are typically not available beforehand. Machine Learning (ML)-based detection method is capable of capturing attacks’ statistical characteristics and is, hence, promising for zero-day attack detection. In this survey paper, a comprehensive review of ML-based zero-day attack detection approaches is conducted, and their ML models, training and testing data sets used, and evaluation results are compared. While significant efforts have been put forth to develop accurate and robust zero-attack detection tools, the existing methods fall short in accuracy, recall, and uniformity against different types of zero-day attacks. Major challenges toward the ML-based methods are identified and future research directions are recommended at last.

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cover image Computer Communications

Computer Communications Volume 198, Issue C

Jan 2023

298 pages

ISSN:0140-3664

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Copyright © 2022.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 January 2023

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Cited By

Li ZZhao D(2024)ZeroD-fender: A Resource-aware IoT Malware Detection Engine via Fine-grained Side-channel AnalysisACM Transactions on Design Automation of Electronic Systems10.1145/368748229:6(1-25)Online publication date: 24-Aug-2024
https://dl.acm.org/doi/10.1145/3687482
Mvula PBranco PJourdan GViktor H(2024)A Survey on the Applications of Semi-supervised Learning to Cyber-securityACM Computing Surveys10.1145/365764756:10(1-41)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3657647
Iannone ESellitto GIaccarino EFerrucci FDe Lucia APalomba F(2024)Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be?ACM Transactions on Software Engineering and Methodology10.1145/365444333:6(1-41)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3654443
Huertas Celdrán AMiguel Sánchez Sánchez Pvon der Assen JSchenk TBovet GMartínez Pérez GStiller B(2024)RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoTIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.340205519(5520-5529)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3402055
Roopak MParkinson STian GRan YKhan SChandrasekaran B(2024)An unsupervised approach for the detection of zero‐day distributed denial of service attacks in Internet of Things networksIET Networks10.1049/ntw2.1213413:5-6(513-527)Online publication date: 21-Nov-2024
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Moreira CMoreira DSales C(2024)A comprehensive analysis combining structural features for detection of new ransomware familiesJournal of Information Security and Applications10.1016/j.jisa.2024.10371681:COnline publication date: 1-Mar-2024
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Wu YHu YWang JFeng MDong AYang Y(2024)An active learning framework using deep Q-network for zero-day attack detectionComputers and Security10.1016/j.cose.2024.103713139:COnline publication date: 16-May-2024
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Novikova EFedorchenko EDanilov ASaenko I(2024)Dataset Generation Methodology: Towards Application of Machine Learning in Industrial Water Treatment SecuritySN Computer Science10.1007/s42979-024-02704-95:4Online publication date: 29-Mar-2024
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Lu HZhao YSong YYang YHe GYu HRen Y(2024)A transfer learning-based intrusion detection system for zero-day attack in communication-based train control systemCluster Computing10.1007/s10586-024-04376-927:6(8477-8492)Online publication date: 1-Sep-2024
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He JYao LLi XKhan MNiu WZhang XLi F(2024)Model-agnostic generation-enhanced technology for few-shot intrusion detectionApplied Intelligence10.1007/s10489-024-05290-854:4(3181-3204)Online publication date: 1-Feb-2024
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