@inproceedings{685aa7fd526a4f29b2a21b71d6d3092c,
title = "Machine Learning for Security: The Case of Side-Channel Attack Detection at Run-time",
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.",
keywords = "AES, Cache-based Side-Channel Attacks, Cryptography, Detection, Flush+Flush, Flush+Reload, Machine Learning, RSA",
author = "Maria Mushtaq and Ayaz Akram and Bhatti, \{Muhammad Khurram\} and Maham Chaudhry and Muneeb Yousaf and Umer Farooq and Vianney Lapotre and Guy Gogniat",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Electronics Circuits and Systems, ICECS 2018 ; Conference date: 09-12-2018 Through 12-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICECS.2018.8617994",
language = "English",
series = "2018 25th IEEE International Conference on Electronics Circuits and Systems, ICECS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "485--488",
booktitle = "2018 25th IEEE International Conference on Electronics Circuits and Systems, ICECS 2018",
}