A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology and Explainable AI as Future Directions

Authors

Sarhad Arisdakessian, Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Canada
Omar Abdel Wahab, Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Canada
Azzam Mourad, Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
Hadi Otrok, Department of EECS, Khalifa University, Abu Dhabi, United Arab Emirates
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Internet of Things Journal

Abstract

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multi-party architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: (1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog and cloud computing layers; (2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of sub-techniques; and (3) propose a comprehensive cybersecurity framework that combines the concepts of Explainable Artificial Intelligence (XAI), federated learning, game theory and social psychology to offer future IoT systems a strong protection against cyberattacks. IEEE

First Page

1

Last Page

1

DOI

10.1109/JIOT.2022.3203249

Publication Date

8-31-2022

Keywords

Cloud computing, Collaborative work, Cybersecurity, Edge computing, Explainable Artificial Intelligence, Federated Learning, Game theory, Game Theory, Internet of Things, Internet of Things, Intrusion detection, Intrusion Detection Systems, Taxonomy

Comments

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Recommended Citation

S. Arisdakessian, O. A. Wahab, A. Mourad, H. Otrok and M. Guizani, "A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology and Explainable AI as Future Directions," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3203249.