15 May 2018 Deep learning-based classification and anomaly detection of side-channel signals
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Abstract
In computer systems, information leaks from the physical hardware through side-channel signals such as power draw. We can exploit these signals to infer the state of ongoing computational tasks without having direct access to the device. This paper investigates the application of recent deep learning techniques to side-channel analysis in both classification of machine state and anomaly detection. We use real data collected from three different devices: an Arduino, a Raspberry Pi, and a Siemens PLC. For classification we compare the performance of a Multi-Layer Perceptron and a Long Short-Term Memory classifiers. Both achieve near-perfect accuracy on binary classification and around 90% accuracy on a multi-class problem. For anomaly detection we explore an autoencoder based model. Our experiments show the potential of using these deep learning techniques in side-channel analysis and cyber-attack detection.
Conference Presentation
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Xiao Wang, Xiao Wang, Quan Zhou, Quan Zhou, Jacob Harer, Jacob Harer, Gavin Brown, Gavin Brown, Shangran Qiu, Shangran Qiu, Zhi Dou, Zhi Dou, John Wang, John Wang, Alan Hinton, Alan Hinton, Carlos Aguayo Gonzalez, Carlos Aguayo Gonzalez, Peter Chin, Peter Chin, } "Deep learning-based classification and anomaly detection of side-channel signals", Proc. SPIE 10630, Cyber Sensing 2018, 1063006 (15 May 2018); doi: 10.1117/12.2311329; https://doi.org/10.1117/12.2311329
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