On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

On the Importance of Pooling Layer Tuning for Profiling Side-Channel Analysis

Wu, L. (TU Delft Cyber Security)
Perin, G. (TU Delft Cyber Security)

Zhou, Jianying (editor)
Ahmed, Chuadhry Mujeeb (editor)
Batina, Lejla (editor)
Chattopadhyay, Sudipta (editor)
Gadyatskaya, Olga (editor)
Jin, Chenglu (editor)
Lin, Jingqiang (editor)
Losiouk, Eleonora (editor)
Luo, Bo (editor)
Majumdar, Suryadipta (editor)
Maniatakos, Mihalis (editor)
Mashima, Daisuke (editor)
Meng, Weizhi (editor)
Picek, Stjepan (editor)
Shimaoka, Masaki (editor)
Su, Chunhua (editor)
Wang, Cong (editor)

2021

In recent years, the advent of deep neural networks opened new perspectives for security evaluations with side-channel analysis. Profiling attacks now benefit from capabilities offered by convolutional neural networks, such as dimensionality reduction and the inherent ability to reduce the trace desynchronization effects. These neural networks contain at least three types of layers: convolutional, pooling, and dense layers. Although the definition of pooling layers causes a large impact on neural network performance, a study on pooling hyperparameters effect on side-channel analysis is still not provided in the academic community. This paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets.

Convolutional neural networks
Deep learning
Pooling
Side-channel analysis

http://resolver.tudelft.nl/uuid:82217934-89cc-489e-be0e-d78ab7721806

https://doi.org/10.1007/978-3-030-81645-2_8

Springer

2022-01-22

9783030816445

Applied Cryptography and Network Security Workshops - ACNS 2021 Satellite Workshops, AIBlock, AIHWS, AIoTS, CIMSS, Cloud S and P, SCI, SecMT, and SiMLA, 2021, Proceedings

satellite workshops held around the 19th International Conference on Applied Cryptography and Network Security, ACNS 2021, 3rd International Workshop on Application Intelligence and Blockchain Security, AIBlock 2021, 2nd International Workshop on Artificial Intelligence in Hardware Security, AIHWS 2021, 3rd International Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2021, 1st International Workshop on Critical Infrastructure and Manufacturing System Security, CIMSS 2021, 3rd International Workshop on Cloud Security and Privacy, Cloud S and P 2021, 2nd International Workshop on Secure Cryptographic Implementation, SCI 2021, 2nd International Workshop on Security in Mobile Technologies, SecMT 2021, 3rd International Workshop on Security in Machine Learning and its Applications, SiMLA 2021, 2021-06-21 → 2021-06-24, Virtual, Online

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 12809 LNCS

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 L. Wu, G. Perin