Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

• Published in: IEEE access Vol. 9; pp. 94668 - 94690
• Main Authors: Zaman, Shakila, Alhazmi, Khaled, Aseeri, Mohammed A., Ahmed, Muhammad Raisuddin, Khan, Risala Tasin, Kaiser, M. Shamim, Mahmud, Mufti
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; attack vector; Computer crime; Cryptography; Electronic devices; Encryption; Fuzzy logic; Internet of Things; IoT applications; IoT protocols; machine leaning; Machine learning; Network security; Nodes; Protocols; Security; Security management
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3089681

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms- branches of Artificial Intelligence (AI)- can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.