A time series approach for profiling attack

Liran Lerman, Gianluca Bontempi, Souhaib Ben Taieb, Olivier Markowitch

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)


The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack is reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the potential source of information available in the temporal dependencies between power values. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives.
Original languageEnglish
Title of host publicationSecurity, Privacy, and Applied Cryptography Engineering
Subtitle of host publicationThird International Conference, SPACE 2013 Kharagpur, India, October 19-23, 2013 Proceedings
EditorsBenedikt Gierlichs, Sylvain Guilley, Debdeep Mukhopadhyay
Place of PublicationBerlin Germany
Number of pages20
ISBN (Electronic)9783642412240
ISBN (Print)9783642412233
Publication statusPublished - 2013
Externally publishedYes
EventInternational Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE 2013) - Indian Institute of Technology, Vikramshila Building, Ramanujan Complex, Kharagpur, India
Duration: 19 Oct 201323 Oct 2013
Conference number: 3rd

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE 2013)
Abbreviated titleSPACE 2013
Internet address

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