An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

• DOI: 10.3390/electronics10212562
• Publication Date: 2021-10-20T11:05:57Z
• Authors: Georgios Zachos; Ismael Essop; Georgios Mantas; Kyriakos Porfyrakis; José C. Ribeiro; Jonathan Rodriguez
• Keywords: Leverage (statistics)

Over the past few years, healthcare sector is being transformed due to rise of Internet Things (IoT) and introduction Medical (IoMT) technology, whose purpose improvement patient’s quality life. Nevertheless, heterogenous resource-constrained characteristics IoMT networks make them vulnerable a wide range threats. Thus, novel security mechanisms, such as accurate efficient anomaly-based intrusion detection systems (AIDSs), considering inherent limitations networks, need be developed before reach their full potential in market. Towards this direction, paper, we propose an effective system (AIDS) for networks. The proposed AIDS aims leverage host-based network-based techniques reliably collect log files from devices gateway, well traffic edge network, while taking into consideration computational cost. rely on machine learning (ML) techniques, computation overhead, order detect abnormalities collected data thus identify malicious incidents network. A set six popular ML algorithms was tested evaluated anomaly AIDS, evaluation results showed which are most suitable.