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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

Standard

A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches. / Allaf, Zirak; Adda, Mo; Gegov, Alexander.

Advances in Intelligent Systems and Computing. ed. / Fei Chao; Steven Schockaert; Qingfu Zhang. Springer, 2017. p. 203-213 (Advances in Intelligent Systems and Computing (AISC); Vol. 650).

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

Harvard

Allaf, Z, Adda, M & Gegov, A 2017, A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches. in F Chao, S Schockaert & Q Zhang (eds), Advances in Intelligent Systems and Computing. Advances in Intelligent Systems and Computing (AISC), vol. 650, Springer, pp. 203-213, The 17th UK Workshop on Computational Intelligence, Cardiff, United Kingdom, 6/09/17. https://doi.org/10.1007/978-3-319-66939-7_17

APA

Allaf, Z., Adda, M., & Gegov, A. (2017). A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches. In F. Chao, S. Schockaert, & Q. Zhang (Eds.), Advances in Intelligent Systems and Computing (pp. 203-213). (Advances in Intelligent Systems and Computing (AISC); Vol. 650). Springer. https://doi.org/10.1007/978-3-319-66939-7_17

Vancouver

Allaf Z, Adda M, Gegov A. A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches. In Chao F, Schockaert S, Zhang Q, editors, Advances in Intelligent Systems and Computing. Springer. 2017. p. 203-213. (Advances in Intelligent Systems and Computing (AISC)). https://doi.org/10.1007/978-3-319-66939-7_17

Author

Allaf, Zirak ; Adda, Mo ; Gegov, Alexander. / A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches. Advances in Intelligent Systems and Computing. editor / Fei Chao ; Steven Schockaert ; Qingfu Zhang. Springer, 2017. pp. 203-213 (Advances in Intelligent Systems and Computing (AISC)).

Bibtex

@inproceedings{e28218159f244ef28d822f434248fd98,
title = "A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches",
abstract = "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.",
keywords = "side-channel attack, machine learning, Flush Reload, Prime Probe, AES",
author = "Zirak Allaf and Mo Adda and Alexander Gegov",
year = "2017",
month = sep,
doi = "10.1007/978-3-319-66939-7_17",
language = "English",
isbn = "978-3319669380",
series = "Advances in Intelligent Systems and Computing (AISC)",
publisher = "Springer",
pages = "203--213",
editor = "Chao, {Fei } and Schockaert, {Steven } and Zhang, {Qingfu }",
booktitle = "Advances in Intelligent Systems and Computing",
note = "The 17th UK Workshop on Computational Intelligence, UKCI 2017 ; Conference date: 06-09-2017 Through 08-09-2017",

}

RIS

TY - GEN

T1 - A comparison study on Flush + Reload and Prime + Probe attacks on AES using machine learning approaches

AU - Allaf, Zirak

AU - Adda, Mo

AU - Gegov, Alexander

PY - 2017/9

Y1 - 2017/9

N2 - 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.

AB - 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.

KW - side-channel attack

KW - machine learning

KW - Flush Reload

KW - Prime Probe

KW - AES

U2 - 10.1007/978-3-319-66939-7_17

DO - 10.1007/978-3-319-66939-7_17

M3 - Conference contribution

SN - 978-3319669380

T3 - Advances in Intelligent Systems and Computing (AISC)

SP - 203

EP - 213

BT - Advances in Intelligent Systems and Computing

A2 - Chao, Fei

A2 - Schockaert, Steven

A2 - Zhang, Qingfu

PB - Springer

T2 - The 17th UK Workshop on Computational Intelligence

Y2 - 6 September 2017 through 8 September 2017

ER -

ID: 7574487