Side Channel Attack using Machine Learning

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dc.contributor.authorAmrouche, Amina
dc.contributor.authorBoubchir, Larbi
dc.contributor.authorYahiaoui, Saïd
dc.date.accessioned2023-06-13T07:56:38Z
dc.date.available2023-06-13T07:56:38Z
dc.date.issued2022-12-15
dc.description.abstractThe overwhelming majority of significant security threats are hardware-based, where the attackers attempt to steal information straight from the hardware that our secure and encrypted software operates on. Unquestionably, side-channel attacks are one of the most severe risks to hardware security. Rather than depending on bugs in the program itself, a side-channel attack exploits information leaked from the program implementation in order to exfiltrate sensitive secret information such as cryptographic keys. A side channel assault could manifest in different ways including electromagnetic radiation, power consumption, timing data, or even acoustic emanation. Ever since the side-channel attacks were introduced in the 1990s, a number of significant attacks on cryptographic implementations utilizing side-channel analysis have emerged, such as template attacks, and attacks based on power analysis and electromagnetic analysis. However, Artificial Intelligence has become more prevalent. Attackers are now more interested in machine learning and deep learning technologies that enable them to interpret the extracted raw data. The aim of this paper is to highlight the main methods of machine learning and deep learning that are used in the most recent studies that deal with different types of side-channel attacks.
dc.identifier.isbn979-8-3503-4671-8
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/972
dc.language.isoen
dc.publisherIEEE
dc.subjectSide-channel attacks
dc.subjectPower analysis
dc.subjectElectro-magnetic analysis
dc.subjectMachine learning
dc.subjectDeep learning
dc.titleSide Channel Attack using Machine Learning
dc.typeConference paper
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