Performance analysis of multilayer perceptron in profiling side-channel analysis

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

Abstract

In profiling side-channel analysis, machine learning-based analysis nowadays offers the most powerful performance. This holds especially for techniques stemming from the neural network family: multilayer perceptron and convolutional neural networks. Convolutional neural networks are often favored as results suggest better performance, especially in scenarios where targets are protected with countermeasures. Multilayer perceptron receives significantly less attention, and researchers seem less interested in this method, narrowing the results in the literature to comparisons with convolutional neural networks. On the other hand, a multilayer perceptron has a much simpler structure, enabling easier hyperparameter tuning and, hopefully, contributing to the explainability of this neural network inner working. We investigate the behavior of a multilayer perceptron in the context of the side-channel analysis of AES. By exploring the sensitivity of multilayer perceptron hyperparameters over the attack’s performance, we aim to provide a better understanding of successful hyperparameters tuning and, ultimately, this algorithm’s performance. Our results show that MLP (with a proper hyperparameter tuning) can easily break implementations with a random delay or masking countermeasures. This work aims to reiterate the power of simpler neural network techniques in the profiled SCA.

Original languageEnglish
Title of host publicationApplied Cryptography and Network Security Workshops
Subtitle of host publicationACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA
EditorsJianying Zhou, Chuadhry Mujeeb Ahmed, Mauro Conti, Eleonora Losiouk, Man Ho Au, Lejla Batina, Zhou Li, Jingqiang Lin, Bo Luo, Suryadipta Majumdar, Weizhi Meng, Martín Ochoa, Stjepan Picek, Georgios Portokalidis, Cong Wang, Kehuan Zhang
Place of PublicationCham
PublisherSpringer
Pages198-216
Number of pages19
ISBN (Electronic)978-3-030-61638-0
ISBN (Print)978-3-030-61637-3
DOIs
Publication statusPublished - 2020
Event2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in Machine Learning and its Applications, SiMLA 2020, held in parallel with the 18th International Conference on Applied Cryptography and Network Security, ACNS 2020 - Rome, Italy
Duration: 19 Oct 202022 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12418
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd ACNS Workshop on Application Intelligence and Blockchain Security, AIBlock 2020, 1st ACNS Workshop on Artificial Intelligence in Hardware Security, AIHWS 2020, 2nd ACNS Workshop on Artificial Intelligence and Industrial IoT Security, AIoTS 2020, 2nd ACNS Workshop on Cloud Security and Privacy, Cloud S and P 2020, 1st ACNS Workshop on Secure Cryptographic Implementation, SCI 2020, 1st ACNS Workshop on Security in Mobile Technologies, SecMT 2020, and 2nd ACNS Workshop on Security in Machine Learning and its Applications, SiMLA 2020, held in parallel with the 18th International Conference on Applied Cryptography and Network Security, ACNS 2020
CountryItaly
CityRome
Period19/10/2022/10/20

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