Intrusion detection for internet of things security: a hidden Markov model based on fuzzy rough set
Yuanting Wang, Guoliang Wu · 2024
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.