Deep learning in side-channel attacks

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.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