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Title: Detecting Malicious Attacks Exploiting Hardware Vulnerabilities Using Performance Counters
Over the past decades, the major objectives of computer design have been to improve performance and to reduce cost, energy consumption, and size, while security has remained a secondary concern. Meanwhile, malicious attacks have rapidly grown as the number of Internet-connected devices, ranging from personal smart embedded systems to large cloud servers, have been increasing. Traditional antivirus software cannot keep up with the increasing incidence of these attacks, especially for exploits targeting hardware design vulnerabilities. For example, as DRAM process technology scales down, it becomes easier for DRAM cells to electrically interact with each other. For instance, in Rowhammer attacks, it is possible to corrupt data in nearby rows by reading the same row in DRAM. As Rowhammer exploits a computer hardware weakness, no software patch can completely fix the problem. Similarly, there is no efficient software mitigation to the recently reported attack Spectre. The attack exploits microarchitectural design vulnerabilities to leak protected data through side channels. In general, completely fixing hardware-level vulnerabilities would require a redesign of the hardware which cannot be backported. In this paper, we demonstrate that by monitoring deviations in microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters, hardware-level attacks such more » as Rowhammer and Spectre can be efficiently detected during runtime with promising accuracy and reasonable performance overhead using various machine learning classifiers. « less
Authors:
;
Award ID(s):
1763793
Publication Date:
NSF-PAR ID:
10154662
Journal Name:
2019 IEEE Computer Society Signature Conference on Computers, Software and Applications (COMPSAC 2019)
Sponsoring Org:
National Science Foundation
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