prikaz prve stranice dokumenta Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja
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master's thesis
Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:385284
Vidulić, Jakov
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Vidulić, J. (2021). Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:385284

Vidulić, Jakov. "Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:385284

Vidulić, Jakov. "Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:385284

Vidulić, J. (2021). 'Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:385284

Vidulić J. Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:385284

J. Vidulić, "Primjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:385284

Metadata
TitlePrimjena dubokog učenja u napadima koji koriste sporedna svojstva kriptografskih uređaja
Title (english)Deep learning in side-channel attacks
AuthorJakov Vidulić
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberMarko Đurasević (član povjerenstva)
Committee memberStjepan Groš (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-12, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom diplomskom radu opisani su napadi koji koriste sporedna svojstva kriptografskih uređaja te su izvršeni profilirajući napadi nad tragovima iz baze podataka ASCAD. Rezultati ́ su uspoređivani s onima iz rada [19]. Implementirani su modeli višeslojnog perceptrona i konvolucijske mreže u svrhu SCA napada. Osim klasifikatora implementiran je i generativni model, CGAN, u svrhu generiranja novih tragova. Pokazalo se da duboki modeli za učinkovit profilirajući napad trebaju veliku količinu tragova te su oba implementirana klasifikatora u tom slučaju dala jako dobre rezultate. Tragovi generirani CGAN mrežom u scenariju malog broja dostupnih tragova nisu doprinijeli kvaliteti napada.
Abstract (english)
This master thesis describes side-channel attacks (SCA) and the conducted profiling at- tacks on traces from the ASCAD database. The results were compared with those from the paper [19]. Multilayer perceptron and convolutional neural network were implemented for the purpose of SCA attack. Apart from the classifiers, a generative model, CGAN, has been implemented for the purpose of generating new traces. It turned out that deep models need a large amount of traces for an effective profiling attack and both implemented classifiers gave very good results in that case. The traces generated by the CGAN network in the scenario where the attacker is in lack of traces did not contribute to the quality of the attack.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:385284
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 14:10:28