Lucas Georget, Vincent Migliore, Vincent Nicomette, Arthur Villard, Frédéric Silvi

Poster: A microarchitectural signals analysis platform to craft Hardware Security Counters

Introduction

Poster: a microarchitectural signals analysis platform to craft hardware security counters. Analyze microarchitectural signals to craft hardware security counters. Detect malicious software, hardware attacks, and Trojans by observing behavioral deviations in computer systems.

Abstract

Detecting malicious software or hardware behavior during the operation of a computer system requires observables from one or more abstraction layers of the system. However, this abstraction tends to limit the ability to detect behavioral deviations, especially for attack classes that exploit vulnerabilities very close to the target hardware. Conversely, too low a level of abstraction tends to significantly increase the complexity of the system model, and therefore poses a number of difficulties for the extraction and selection of relevant observables for a given class of attack.In particular, processor performance counters have been used as an indirect means of observing microarchitecture behavior and detecting software attempting to exploit hardware vulnerabilities. In order to improve the various detection methods, we propose the construction of hardware metrics designed from the outset for security, by studying the correlation between signals from the microarchitecture and the various classes of attack in the literature, targeting both usual and industrial systems. By extension, this work aims to detect attacks originating from hardware Trojans, the latter having the effect of changing the behavior of a given microarchitecture.

Review

The submission, titled "Poster: A microarchitectural signals analysis platform to craft Hardware Security Counters," addresses a critical challenge in computer system security: the effective detection of sophisticated malicious software and hardware behaviors. The abstract clearly articulates the limitations of current detection approaches, which either suffer from overly high abstraction that misses granular attacks or excessive complexity when delving into low-level microarchitectural details. The central proposal, therefore, is highly pertinent: to move beyond repurposing generic performance counters by designing hardware metrics explicitly for security, leveraging systematic correlation studies between microarchitectural signals and various attack classes. This proactive approach to designing "security counters" from the ground up represents a significant strength and a promising direction for hardware security. By directly analyzing and correlating specific microarchitectural signals with known attack vectors, including those posed by hardware Trojans, the proposed work has the potential to yield more precise, efficient, and robust detection capabilities. The ambition to apply this methodology to both common computing systems and specialized industrial environments further highlights the broad relevance and potential impact of crafting security-centric observability at the microarchitectural level. This could significantly enhance our ability to detect advanced threats that exploit vulnerabilities very close to the underlying hardware. While the abstract provides a compelling high-level vision, a comprehensive evaluation would necessitate a deeper dive into the proposed methodology. Specific areas requiring further elaboration include the detailed experimental design for "studying the correlation," such as the precise microarchitectural signals to be observed, the specific attack classes to be analyzed, and the quantitative metrics used to establish these correlations. Furthermore, it would be crucial to understand how the proposed "platform" manages or mitigates the inherent complexity of low-level observation, which the abstract itself identifies as a significant hurdle. Elucidating the practical implementation challenges, potential overheads, and validation strategies for these novel security counters would be essential for demonstrating the feasibility and real-world applicability of this innovative approach.

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