k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

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dc.contributor.authorMartinasek, Zdenek
dc.contributor.authorZeman, Vaclav
dc.contributor.authorMalina, Lukas
dc.contributor.authorMartinasek, Josef
dc.coverage.issue2cs
dc.coverage.volume25cs
dc.date.accessioned2016-08-12T06:57:38Z
dc.date.available2016-08-12T06:57:38Z
dc.date.issued2016-06cs
dc.description.abstractPower analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first containing the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary k-NN (k-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.en
dc.formattextcs
dc.format.extent365-382cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2016 vol. 25, č. 2, s. 365-382. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2016.0365en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/63060
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2016/16_02_0365_0382.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectPower Analysisen
dc.subjectMachine Learningen
dc.subjectTemplate Attacken
dc.subjectComparisonen
dc.subjectSmart Cardsen
dc.titlek-Nearest Neighbors Algorithm in Profiling Power Analysis Attacksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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