Artificial intelligence empowered threat detection in the Internet of Things: A systematic review. (2nd March 2022)

• Record Type: Journal Article
• Authors: Soltani, Nasim; Rahmani, Amir Masoud; Bohlouli, Mahdi; Hosseinzadeh, Mehdi
• Is Part Of: Concurrency and computation. Volume 34:Number 22(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-02
• Subjects: artificial intelligence approach -- cyber‐security -- Internet of Things intrusion detection -- malware attacks; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6894
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Internet of Things (IoT) is a new phenomenon that proposes novel business opportunities. IoT allows the world to be programmable and might provide several benefits for organizations. Based on the IoT survey, cyber‐security issues are among the most extensive and complicated challenges faced by IoT devices. Threat detection is considered a preventive measure against malware threats, ransomware, and attacks, which become more serious each year because of the dramatic rise in malware attacks. This article investigates threat detection techniques that fall into three categories: malware detection, attack detection, and ransomware detection, published from 2017 to August 2021. We examine solutions, techniques, features, classifiers, and tools proposed by IoT researchers. Some questions are proposed, and answering the questions may help the researchers suggest a more efficient solution in future works. Furthermore, the achievement and disadvantages of each study are discussed. Finally, based on the reviewed studies, some open challenges and practical measures to future directions are suggested, worth further studying and researching threat detection techniques in the IoT.