Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

Abstract

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.