Call for Papers: Machine Learning Techniques in Side-channel Attacks
* Debdeep Mukhopadhyay, Indian Institute of Technology, Kharagpur, India, firstname.lastname@example.org
* Stjepan Picek, Radboud University, The Netherlands, email@example.com
In side-channel analysis (SCA), the attacker exploits weaknesses in the physical implementations of cryptographic algorithms. In the last decade, profiling SCA based on machine learning proved very successful in breaking cryptographic implementations in various settings. Still, despite all the successful results, there are many open questions. With all the diverse strategies and techniques in machine learning-based side-channel analysis, it is not obvious how effective and efficient are the different approaches and how they compare to each other.
In addition, it is hard to identify the primary challenges as they are typically device-or threat model-specific. The goal of the special issue is to help increase the awareness about machine learning-based SCA and gather researchers’ latest results in this challenging domain. Considering the above challenges, the scope of this Special Issue of Springer JCEN deals with the following topics (but not limited to):
- Machine/deep learning-based side-channel attacks
- Dataset design and analysis
- Novel countermeasures against machine/deep learning-based SCA
- Machine/deep learning-based SCA for reverse-engineering, hardware detection, ...
- Novel applications of machine/deep learning in SCA
- Explainability of machine/deep learning in SCA
- Comparisons with non-machine learning-based SCA
- Survey and Systematization-of-Knowledge papers
All original manuscripts or revisions to the journal of Cryptographic Engineering (JCEN) must be submitted online. Please be sure to read the submission guidelines before submitting: https://www.springer.com/journal/13389/submission-guidelines
To submit, go to the journal home page: https://www.springer.com/journal/13389 and select “Submit Manuscript.” When submitting, on the details tab, select the "Special Issue on Machine Learning Techniques in Side-channel Attacks" to ensure that the article is considered for this special issue. Authors must also mention the same in their submission cover letter.
Submitted articles must not have been previously published or currently submitted for publication elsewhere. For previously published conference papers, it is required that submissions to the special issue have at least 40% new content. Submissions that do not meet this requirement will be rejected.
Open to submissions: June 29th, 2022
Submission deadline: October 1, 2022
Revision: January 3, 2023
2nd revision: March 3, 2023
Acceptance notification: June 4, 2023
Online Publication: mid-July 2023