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.

Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 50003 Hradec Kralove Czech Republic

Directorate of Information Technology Koya University Koya 44023 Iraq

Graduate School Hiroshima University Kagamiyama Higashihiroshima 739 8511 Japan

i SOMET Incorporated Association Morioka 020 0104 Japan

Institute of IR4 0 Universiti Kebangsaan Malaysia Bangi 43600 Malaysia

Malaysia Japan International Institute of Technology University Teknologi Malaysia Kuala Lumpur 54100 Malaysia

Media and Games Center of Excellence Universiti Teknologi Malaysia Skudai 81310 Malaysia

Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur 54100 Malaysia

Regional Research Center Iwate Prefectural University Takizawa 020 0693 Japan

School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai 81310 Malaysia

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