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A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches

Research output: Chapter in Book/Report/Conference proceedingConference contribution

AES, ElGamal are two examples of algorithms that have been developed in cryptography to protect data in a variety of domains including native and cloud systems, and mobile applications. There has been a good deal of research into the use of side channel attacks on these algorithms. This work has conducted an experiment to detect malicious loops inside Flush+Reload and Prime+Prob attack programs against AES through the exploitation of Hardware Performance Counters (HPC). This paper examines the accuracy and eciency of three machine learning algorithms: Neural Network (NN); Decision Tree C4.5; and K Nearest Neighbours (KNN). The study also shows how Standard Performance Evaluation Corporation (SPEC) CPU2006 benchmarks impact predictions.
Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
EditorsFei Chao, Steven Schockaert, Qingfu Zhang
PublisherSpringer
Pages203-213
Number of pages11
ISBN (Electronic)978-319669397
ISBN (Print)978-3319669380
DOIs
Publication statusPublished - Sep 2017
EventThe 17th UK Workshop on Computational Intelligence - Cardiff University, Cardiff, United Kingdom
Duration: 6 Sep 20178 Sep 2017

Publication series

NameAdvances in Intelligent Systems and Computing (AISC)
PublisherSpringer, Cham
Volume650
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceThe 17th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2017
CountryUnited Kingdom
CityCardiff
Period6/09/178/09/17

Documents

  • UKCI_2017_PAPER

    Rights statement: This is a pre-copyedited version of a contribution published in Advances in Computational Intelligence Systems. UKCI 2017. Fei Chao, Steven Schockaert, Qingfu Zhang (eds). Published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-66939-7_17.

    Accepted author manuscript (Post-print), 379 KB, PDF document

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