Utilising honeypots and machine learning to mitigate unknown threats in IoT

G. Bendiab*, J. Rose, M. Swann, S. Shiaeles

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Abstract

IoT security has now emerged as one of the most important issue in network security. Conventional security techniques, such as firewalls and signature-based intrusion detection systems, have proven ineffective in protecting IoT networks from increasingly sophisticated attack and malware. Due to these constraints, researchers have been compelled to build novel intrusion detection solutions utilising various technologies such as IoT Honeypots and Machine Learning (ML). This chapter describes a novel approach to detect malicious network traffic that employs a honeypot and machine learning. The IoT honeypot system is used to gather intelligence about attacks that target IoT devices. The data gathered are used to understand the attackers’ weapons, strategies and new techniques utilised. It is also used to train the machine learning model used on IDS on a continuous basis to improve its detection accuracy. This method is most successful against unknown and zero-day attacks on IoT computers.
Original languageEnglish
Title of host publicationSecurity Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation
EditorsGohar Sargsyan, Dimitrios Kavallieros, Nicholas E. Kolokotronis
PublisherNow Publishers Inc
Chapter6
Pages92-105
Number of pages14
ISBN (Electronic)9781680838350
ISBN (Print)9781680838343
DOIs
Publication statusPublished - 15 Mar 2022

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