Profiling side-channel attacks on cryptographic algorithms

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dc.check.embargoformatNot applicableen
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dc.check.opt-outNot applicableen
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dc.contributor.advisorMarnane, William P.en
dc.contributor.authorHanley, Neil John
dc.contributor.funderEnterprise Irelanden
dc.date.accessioned2015-08-18T15:43:10Z
dc.date.available2015-08-18T15:43:10Z
dc.date.issued2014
dc.date.submitted2014
dc.description.abstractTraditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.en
dc.description.sponsorshipEnterprise Ireland (Informatics Commercialisation Initiative)en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHanley, N. J. 2014. Profiling side-channel attacks on cryptographic algorithms. PhD Thesis, University College Cork.en
dc.identifier.endpage187
dc.identifier.urihttps://hdl.handle.net/10468/1921
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2014, Neil J. Hanley.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectAESen
dc.subjectRSAen
dc.subjectECCen
dc.subjectProfiling attacken
dc.subjectSide-channel analysisen
dc.subjectMachine learningen
dc.subjectPower analysisen
dc.subjectTemplate attacken
dc.subjectNeural networksen
dc.subjectSupport vector machinesen
dc.thesis.opt-outfalse
dc.titleProfiling side-channel attacks on cryptographic algorithmsen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorl.marnane@ucc.ie
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