Share Email Print

Proceedings Paper

Deep learning-based classification and anomaly detection of side-channel signals
Author(s): Xiao Wang; Quan Zhou; Jacob Harer; Gavin Brown; Shangran Qiu; Zhi Dou; John Wang; Alan Hinton; Carlos Aguayo Gonzalez; Peter Chin
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 15 May 2018
PDF: 8 pages
Proc. SPIE 10630, Cyber Sensing 2018, 1063006 (15 May 2018); doi: 10.1117/12.2311329
Show Author Affiliations
Xiao Wang, Boston Univ. (United States)
Quan Zhou, Boston Univ. (United States)
Jacob Harer, Boston Univ. (United States)
Gavin Brown, Boston Univ. (United States)
Shangran Qiu, Boston Univ. (United States)
Zhi Dou, Boston Univ. (United States)
John Wang, PFP Cybersecurity (United States)
Alan Hinton, PFP Cybersecurity (United States)
Carlos Aguayo Gonzalez, PFP Cybersecurity (United States)
Peter Chin, Boston Univ. (United States)

Published in SPIE Proceedings Vol. 10630:
Cyber Sensing 2018
Igor V. Ternovskiy; Peter Chin, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?