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IoT Malware Classification Based on System Calls

Hoang, Dang Kien and Nguyen, Dai Tho and Vu, Duy Loi (2020) IoT Malware Classification Based on System Calls. In: The 2020 IEEE-RIVF International Conference on Computing and Communications Technologies, Ho Chi Minh. (In Press)

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IoT devices play an important role in the industrial revolution 4.0. However, this type of device may exhibit specific security vulnerabilities that can be easily exploited to cause botnet attacks and other malicious activities. In this paper, we introduce a new method for classification and clustering of IoT malware behaviors through system call monitoring. Our method is constructed from multiple one-class SVM classifiers and has the ability to classify known malware with F1-Score over 98% and probability to detect unknown malware up to 97%. Unknown malware instances with similar behaviors can also be grouped together so new classes of malware will be discovered.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Faculty of Information Technology (FIT)
Depositing User: Kien Hoang Dang
Date Deposited: 18 Jul 2020 01:59
Last Modified: 18 Jul 2020 01:59

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