AI/ML driven intrusion detection framework for IoT enabled cold storage monitoring system

An IoT-based monitoring system remotely controls and manages intelligent environments. Due to wireless communication, deployed sensor nodes are more vulnerable to attacks. An intrusion detection system is an efficient mechanism to detect malicious traffic and prevent abnormal activities. This article suggests an intrusion detection framework for the cold storage monitoring system. The temperature is the main parameter that affects the environment and harms stored products. A malicious node injects false data that manipulates temperature and forwards manipulated data. It also floods the data to neighbor nodes. In this work, data are generated and collected for intrusion detection. Two machine learning techniques have been applied: supervised learning (Bayesian rough set) and unsupervised learning (micro-clustering). The proposed method shows better performance than existing methods.