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A review of anomaly detection techniques leveraging side-channel emissions
Author(s): Devin Spatz; Devin Smarra; Igor Ternovskiy
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Paper Abstract

The low-cost, flexible nature of Internet-of-Things (IoT) hardware has resulted in widespread usage in a variety of applications from smart-home systems to industrial process-regulation controllers. As the number of networkconnected IoT devices has proliferated, they have become increasingly likely to be the target of widespread cyber-attacks. Since these devices are often low-resource, embedded or bare metal systems, conventional profiling techniques used by Personal Computers (PCs) and workstations have become highly impractical means for security. As a result, an IoT device could provide intruders with an unprotected backdoor into a network. Effectively protecting IoT hardware requires that alternative security protocols be developed and utilized to protect the IoT and the networks they are integrated with. One potential way of improving the security of IoT devices is by monitoring their side-channel emissions to observe device behavior. As these devices operate, they will produce multi-spectral phenomenon, or side-channel emissions, that correlate with program execution. By combining spectral analysis techniques with powerful machine learning algorithms, side-channel emissions can be utilized to bolster IoT device security and deny an intruder access to the network. This paper will review current state-of-the-art techniques used to monitor and classify the behavior of IoT devices. The paper will conclude by discussing several real-world applications presented in literature that have been shown to benefit from these techniques.

Paper Details

Date Published: 17 May 2019
PDF: 8 pages
Proc. SPIE 11011, Cyber Sensing 2019, 110110E (17 May 2019); doi: 10.1117/12.2521450
Show Author Affiliations
Devin Spatz, Defense Engineering Corp. (United States)
Air Force Research Lab. (United States)
Devin Smarra, Defense Engineering Corp. (United States)
Air Force Research Lab. (United States)
Igor Ternovskiy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 11011:
Cyber Sensing 2019
Igor V. Ternovskiy; Peter Chin, Editor(s)

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