Abstract
Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems. However, the current fog-based systems cannot handle the ever-increasing IoMT’s big data. Moreover, they are not lightweight and are designed for network attack detection only. In this work, a hybrid (for host and network) lightweight system is proposed for early attack detection in the IoMT fog. In an adaptive online setting, six different incremental classifiers were implemented, namely a novel Weighted Hoeffding Tree Ensemble (WHTE), Incremental K-Nearest Neighbors (IKNN), Incremental Naïve Bayes (INB), Hoeffding Tree Majority Class (HTMC), Hoeffding Tree Naïve Bayes (HTNB), and Hoeffding Tree Naïve Bayes Adaptive (HTNBA). The system was benchmarked with seven heterogeneous sensors and a NetFlow data infected with nine types of recent attack. The results showed that the proposed system worked well on the lightweight fog devices with ~100% accuracy, a low detection time, and a low memory usage of less than 6 MiB. The single-criteria comparative analysis showed that the WHTE ensemble was more accurate and was less sensitive to the concept drift.
Highlights
IntroductionSmart health systems such as the Internet of Medical Things (IoMT) and Medical
We propose a hybrid lightweight fog-based multi-attack detection system with early detection capability, due to adaptive learning techniques
The detection system needed to simultaneously use all the Internet of Things (IoTs) sensor readings to capture any malicious readings for the host-based attack detection
Summary
Smart health systems such as the Internet of Medical Things (IoMT) and Medical. Cyber–Physical Systems (MCPSs) are a subset of the Internet of Things (IoTs) [1]. They are gaining popularity via simple fitness gadgets connecting athletes to their smartphone devices and cloud services [2]. IoMT is a broad technology, incorporating various products and platforms, including implanted devices, eldercare wearables for monitoring [3], internet-connected clinical equipment, and remote-surgery hospital rooms [4].
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