Improving Performance and Mitigating Fault Attacks Using Value Prediction

We present Value Prediction for Security (VPsec), a novel hardware-only framework to counter fault attacks in modern microprocessors, while preserving the performance benefits of Value Prediction (VP.) VP is an elegant and hitherto mature microarchitectural performance optimization, which aims to pr...

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Bibliographic Details
Main Authors: Rami Sheikh, Rosario Cammarota
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Cryptography
Subjects:
Online Access:http://www.mdpi.com/2410-387X/2/4/27
Description
Summary:We present Value Prediction for Security (VPsec), a novel hardware-only framework to counter fault attacks in modern microprocessors, while preserving the performance benefits of Value Prediction (VP.) VP is an elegant and hitherto mature microarchitectural performance optimization, which aims to predict the data value ahead of the data production with high prediction accuracy and coverage. Instances of VPsec leverage the state-of-the-art Value Predictors in an embodiment and system design to mitigate fault attacks in modern microprocessors. Specifically, VPsec implementations re-architect any baseline VP embodiment with fault detection logic and reaction logic to mitigate fault attacks to both the datapath and the value predictor itself. VPsec also defines a new mode of execution in which the predicted value is trusted rather than the produced value. From a microarchitectural design perspective, VPsec requires minimal hardware changes (negligible area and complexity impact) with respect to a baseline that supports VP, it has no software overheads (no increase in memory footprint or execution time), and it retains most of the performance benefits of VP under realistic attacks. Our evaluation of VPsec demonstrates its efficacy in countering fault attacks, as well as its ability to retain the performance benefits of VP on cryptographic workloads, such as OpenSSL, and non-cryptographic workloads, such as SPEC CPU 2006/2017.
ISSN:2410-387X