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

Zirak Allaf; Mo Adda; Alexander Gegov

doi:10.1007/978-3-319-66939-7_17

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

Zirak Allaf
, Mo Adda
, Alexander Gegov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1964 Downloads (Pure)

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.

Original language	English
Title of host publication	Advances in Intelligent Systems and Computing
Editors	Fei Chao, Steven Schockaert, Qingfu Zhang
Publisher	Springer
Pages	203-213
Number of pages	11
ISBN (Electronic)	978-319669397
ISBN (Print)	978-3319669380
DOIs	https://doi.org/10.1007/978-3-319-66939-7_17
Publication status	Published - Sept 2017
Event	The 17th UK Workshop on Computational Intelligence - Cardiff University, Cardiff, United Kingdom Duration: 6 Sept 2017 → 8 Sept 2017

Publication series

Name	Advances in Intelligent Systems and Computing (AISC)
Publisher	Springer, Cham
Volume	650
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	The 17th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2017
Country/Territory	United Kingdom
City	Cardiff
Period	6/09/17 → 8/09/17

Keywords

side-channel attack
machine learning
Flush Reload
Prime Probe
AES

Access to Document

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

UKCI_2017_PAPER
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

Cite this

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

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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",

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booktitle = "Advances in Intelligent Systems and Computing",

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note = "The 17th UK Workshop on Computational Intelligence, UKCI 2017 ; Conference date: 06-09-2017 Through 08-09-2017",

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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

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 proceeding › Conference contribution

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

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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

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KW - Prime Probe

KW - AES

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

M3 - Conference contribution

SN - 978-3319669380

T3 - Advances in Intelligent Systems and Computing (AISC)

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BT - Advances in Intelligent Systems and Computing

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ER -

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

Abstract

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Conference

Keywords

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Cite this