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AuthorAl-Garadi M.A.
AuthorMohamed A.
AuthorAl-Ali A.K.
AuthorDu X.
AuthorAli I.
AuthorGuizani M.
Available date2022-04-21T08:58:24Z
Publication Date2020
Publication NameIEEE Communications Surveys and Tutorials
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/COMST.2020.2988293
URIhttp://hdl.handle.net/10576/30089
AbstractThe Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions. 1998-2012 IEEE.
SponsorQatar Foundation;Qatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAuthentication
Cryptography
Deep learning
Learning systems
Machinery
Network security
Surveys
Application security
Future research directions
History of computing
Intelligence systems
Internet of thing (IOT)
Internet of Things (IOT)
Machine intelligence
Security challenges
Internet of things
TitleA Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
TypeArticle
Pagination1646-1685
Issue Number3
Volume Number22
dc.accessType Abstract Only


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