Deep learning-based classification and anomaly detection of side-channel signals

Xiao Wang; Quan Zhou; Jacob Harer; Gavin Brown; Shangran Qiu; Zhi Dou; John Wang; Alan Hinton; Carlos Aguayo Gonzalez; Peter Chin

doi:10.1117/12.2311329

15 May 2018 Deep learning-based classification and anomaly detection of side-channel signals

Xiao Wang, Quan Zhou, Jacob Harer, Gavin Brown, Shangran Qiu, Zhi Dou, John Wang, Alan Hinton, Carlos Aguayo Gonzalez, Peter Chin

Author Affiliations +

Proceedings Volume 10630, Cyber Sensing 2018; 1063006 (2018) https://doi.org/10.1117/12.2311329
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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

Citation Download Citation

Xiao Wang, Quan Zhou, Jacob Harer, Gavin Brown, Shangran Qiu, Zhi Dou, John Wang, Alan Hinton, Carlos Aguayo Gonzalez, and Peter Chin "Deep learning-based classification and anomaly detection of side-channel signals", Proc. SPIE 10630, Cyber Sensing 2018, 1063006 (15 May 2018); https://doi.org/10.1117/12.2311329

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