Al-Garadi, Mohammed Ali and Mohamed, Amr and Al-Ali, Abdulla Khalid and Du, Xiaojiang and Ali, Ihsan and Guizani, Mohsen (2020) A survey of machine and deep learning methods for Internet of Things (IoT) security. IEEE Communications Surveys & Tutorials, 22 (3). pp. 1646-1685. ISSN 1553-877X, DOI https://doi.org/10.1109/COMST.2020.2988293.
Full text not available from this repository.Abstract
The 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.
| Item Type: | Article |
|---|---|
| Funders: | NPRP from the Qatar National Research Fund (a member of Qatar Foundation) [Grant No: 8-408-2-172 & 12S-0305-190231] |
| Uncontrolled Keywords: | Machine learning; Internet of Things; Tutorials; Electronic mail; Classification algorithms; Encryption; Deep learning; Machine learning; Internet of Things security; Security based intelligence; IoT |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Mrs. Siti Mawarni Salim |
| Date Deposited: | 19 Sep 2022 03:32 |
| Last Modified: | 19 Sep 2022 03:32 |
| URI: | http://eprints.um.edu.my/id/eprint/31712 |
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