Abstract
Internet of Medical Things (IoMT) technology has emerged from the introduction of the Internet of Things in the healthcare sector. However, the resource-constrained characteristics and heterogeneity of IoMT networks make these networks susceptible to various types of threats. Thus, it is necessary to develop novel security solutions (e.g., efficient and accurate Anomaly-based Intrusion Detection Systems), considering the inherent limitations of IoMT networks, before these networks reach their full potential in the market. In this paper, we propose an AIDS specifically designed for resource-constrained devices within IoMT networks. The proposed lightweight AIDS leverages novelty detection and outlier detection algorithms instead of conventional classification algorithms to achieve (a) enhanced detection performance against both known and unknown attack patterns and (b) minimal computational costs.
Original language | English |
---|---|
Article number | 1216 |
Number of pages | 20 |
Journal | Sensors |
Volume | 25 |
Issue number | 4 |
DOIs | |
Publication status | Published - 17 Feb 2025 |
Keywords
- anomaly-based intrusion detection
- dataset generation
- Internet of Medical Things (IoMT)
- intrusion detection system (IDS)
- machine learning algorithms
- novelty detection algorithms
- outlier detection algorithms