Abstract
Machine learning algorithms fall prey to adversarial examples. As profiling side-channel attacks are seeing rapid adoption of machine learning-based approaches that can even defeat commonly used side-channel countermeasures, we investigate the potential of adversarial example as a defense mechanism. We show that adversarial examples have the potential to serve as a countermeasure against machine learning-based side-channel attacks. Further, we exploit the transferability property to show that a common adversarial example can act as a countermeasure against a range of machine learning-based side-channel classifiers.
Original language | English |
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Title of host publication | CCS '19 |
Subtitle of host publication | Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 2673-2675 |
Number of pages | 3 |
ISBN (Print) | 978-1-4503-6747-9 |
DOIs | |
Publication status | Published - 2019 |
Event | 26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019 - London, United Kingdom Duration: 11 Nov 2019 → 15 Nov 2019 |
Conference
Conference | 26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019 |
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Country/Territory | United Kingdom |
City | London |
Period | 11/11/19 → 15/11/19 |
Keywords
- Adversarial Examples
- Machine Learning
- Profiled Attacks
- Side-channel Analysis