Learning From A Big Brother: Mimicking Neural Networks in Profiled Side-channel Analysis

Daan van der Valk, Marina Krcek, Stjepan Picek, Shivam Bhasin

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)


Recently, deep learning has emerged as a powerful technique for side-channel attacks, capable of even breaking common countermeasures. Still, trained models are generally large, and thus, performing evaluation becomes resource-intensive. The resource requirements increase in realistic settings where traces can be noisy, and countermeasures are active. In this work, we exploit mimicking to compress the learned models. We demonstrate up to 300 times compression of a state-of-the-art CNN. The mimic shallow network can also achieve much better accuracy as compared to when trained on original data and even reach the performance of a deeper network.
Original languageEnglish
Title of host publication2020 57th ACM/IEEE Design Automation Conference, DAC 2020
Place of PublicationPiscataway
Number of pages6
ISBN (Electronic)9781450367257
ISBN (Print)978-1-7281-5802-0
Publication statusPublished - 2020
EventDAC 2020: 57th ACM/IEEE Design Automation Conference - San Francisco, United States
Duration: 20 Jul 202024 Jul 2020
Conference number: 57th

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100x


ConferenceDAC 2020
Country/TerritoryUnited States
CitySan Francisco


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