@inproceedings{0fcd94affa1b4a37b95cc3b3da2a78ad,
title = "Vulnerability Assessment of the Rowhammer Attack Using Machine Learning and the gem5 Simulator - Work in Progress",
abstract = "Modern computer memories have been shown to have reliability issues. The main memory is the target of a security attack called Rowhammer, which causes bit flips in adjacent victim cells of aggressor rows. Multiple mitigation techniques have been proposed to counter this issue, but they all come at a non-negligible cost of performance and/or silicon surface. Some techniques rely on a detection mechanism using row access counters to trigger automatic defenses. In this paper, we propose a tool to build a system-specific detection mechanism using gem5 to simulate the system and Machine Learning to detect the attack by analyzing hardware event traces. The detection mechanism built with our tool shows high accuracy (over 99.5\%) and low latency (maximum 474μs to classify when running offline in software) to detect an attack before completion.",
keywords = "computer architecture, deep learning, dram, gem5, machine-learning, neural networks, rowhammer, rowhammer detection, security, security assessments",
author = "Lo{\"i}c France and Maria Mushtaq and Florent Bruguier and David Novo and Pascal Benoit",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 1st ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, SaT-CPS 2021 ; Conference date: 28-04-2021",
year = "2021",
month = apr,
day = "28",
doi = "10.1145/3445969.3450425",
language = "English",
series = "SAT-CPS 2021 - Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "104--109",
booktitle = "SAT-CPS 2021 - Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems",
}