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    "title": "DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS",
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            "value": "Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber-attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregationaggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances",
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                "unstructured": "M. A. Al-Garadi, A. Mohamed, A. Al-Ali, X. Du, and M. Guizani, ―A survey of machine and deep learning methods for internet of things (IoT) security,‖ arXiv preprint arXiv:1807.11023, 2018"
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                "unstructured": "H. Ning, H. Liu, and L. Yang, ―Cyber-entity security in the internet of things,‖ Computer, p. 1, 2013"
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                "doi": "10.14569/ijacsa.2018.090349",
                "unstructured": "H. A. Abdul-Ghani, D. Konstantas, and M. Mahyoub, ―A comprehensive IoT attacks survey based on a building-blocked reference model,‖ International Journal of Advanced Computer Science and Applications(IJACSA), vol. 9(3), 2018"
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                "doi": "10.1109/jiot.2017.2711426",
                "unstructured": "H. Guo, S. Li, B. Li, Y. Ma, and X. Ren, ―A new learning Automata-Based pruning method to train deep neural networks,‖ IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3263–3269, Oct. 2018"
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