Intrusion detection for internet of things security: a hidden Markov model based on fuzzy rough set

Yuanting Wang; Guoliang Wu

doi:10.1117/12.3052573

19 November 2024 Intrusion detection for internet of things security: a hidden Markov model based on fuzzy rough set

Yuanting Wang, Guoliang Wu

Proceedings Volume 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024); 133970V (2024) https://doi.org/10.1117/12.3052573
Event: 4th International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 2024, Guiyang, China

Abstract

Internet of Things (IoT) devices are vulnerable to various cyber-attacks. Therefore, it is significantly crucial to design an effective intrusion detection system (IDS) for IoT security. However, IoT devices have limited resources such as computing resources needing to handle a great of data, which further increases the difficulty of precise intrusion detection. Moreover, most IDSs lack transparency. This study develops a feature selection-based hidden Markov model (HMM) for intrusion detection. We first establish a modified approach based on a fuzzy rough set for feature selection to select optimal features so that the computational burden can be reduced. Furthermore, an interpretable intrusion detection model is established based on the HMM. In addition, intrusion detection is achieved in the presence of partially missing data through the expectation-maximization algorithm. According to the simulation results, the proposed method is able to detect intrusion detection in IoT networks effectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanting Wang and Guoliang Wu "Intrusion detection for internet of things security: a hidden Markov model based on fuzzy rough set", Proc. SPIE 13397, Fourth International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), 133970V (19 November 2024); https://doi.org/10.1117/12.3052573

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