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Title	Malicious Activity Detection In Iot Networks: A Nature-Inspired Approach
ID_Doc	36205
Authors	Procopiou A.; Chen T.M.
Year	2022
Published	EAI/Springer Innovations in Communication and Computing
DOI	http://dx.doi.org/10.1007/978-3-030-90708-2_4
Abstract	One of the most fundamental technologies used for the realisation of smart cities is the Internet of Things (IoT). While IoT brings multiple benefits, it introduces its own vulnerabilities which can be exploited in new types of cyber-attacks. Artificial intelligence (AI) is a popular solution for potentially identifying malicious behaviours indicative of cyber-attacks. This chapter describes a sub-category of AI algorithms inspired by nature, biology, and social systems. Nature has often found effective and elegant solutions to many common problems. Nature-inspired AI algorithms can be useful for intrusion detection for IoT networks, in contrast to traditional signature-based or anomaly-based detection. This chapter gives an overview of the most popular nature-inspired algorithms for intrusion detection and identifies open issues for future research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Intrusion detection; IoT networks; Nature algorithms

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