Machine Learning in IoT Security: Current Solutions and Future Challenges

By Fatima Hussain, Rasheed Hussain, Syed Ali Hassan, Ekram Hossain. IEEE Communications Surveys and Tutorials, 2019

695 citations recorded by the source corpus.

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, can be leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. Finally, we discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. We also discuss several future research directions for ML- and DL-based IoT security.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bfdad8066d2b8dff3e9bae43715724813ad5dbc3

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Citations in the corpus

Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey (2023, background): “These reference architectures serve as invaluable resources for organizations and developers embarking on IoT initiatives [32,33].”
AI for Next Generation Computing: Emerging Trends and Future Directions (2022, neutral): “• Security and privacy: Fog infrastructure is critical to determining the security of the applications because of security concerns such as information degradation, identity disclosure, replay, and denial of service assaults [94].”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “Hussain et al. [22] went beyond the NIDS setting and evaluated ML and DL techniques to address a wide range of security issues in IoT, such as malware analysis, authentication and access control, and intrusion detection.”
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, background): “On the other hand, there are few surveys [43], [44] that have briefly discussed federated learning as a solution to some existing problems (e.”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “1A more detailed description of the ML and DL techniques can be found in [66].”
Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions (2022, background): “The authors examine the security requirements, attack vectors, and other discussions in [61], focused on computer learn-”