Machine Learning For Security: The Case of Side-Channel Attack Detection at Run-time

Maria Mushtaq 1 Ayaz Akram 2 Muhammad Khurram Bhatti Maham Chaudhry Muneeb Yousaf Umer Farooq Vianney Lapotre 3 Guy Gogniat 4
1 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Lab-STICC_UBS_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : This paper presents experimental evaluation and comparative analysis on the use of various Machine Learning (ML) models for detecting Cache-based Side Channel Attacks (CSCAs) in Intel's x86 architecture. The paper provides performance evaluation of ML models based on run-time detection accuracy, speed, computational overhead, and distribution of error in terms of false positives and false negatives. Experiments are performed using state-of-the-art CSCAs namely; Flush+Reload and Flush+Flush attacks, under realistic load conditions on RSA and AES crypto-systems. The paper provides quantitative & qualitative analysis of at least 12 ML models being used for CSCA detection for the first time.
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https://hal.archives-ouvertes.fr/hal-01876792
Contributor : Maria Mushtaq <>
Submitted on : Tuesday, September 18, 2018 - 6:39:43 PM
Last modification on : Monday, February 25, 2019 - 3:14:12 PM

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Maria Mushtaq, Ayaz Akram, Muhammad Khurram Bhatti, Maham Chaudhry, Muneeb Yousaf, et al.. Machine Learning For Security: The Case of Side-Channel Attack Detection at Run-time. ICECS-2018, Dec 2018, Bordeaux, France. ⟨hal-01876792⟩

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