| Title | EVAX: Towards a Practical, Pro-active & Adaptive Architecture for High Performance & Security |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Ajorpaz, Samira Mirbagher, Moghimi, Daniel, Collins, Jeffrey Neal, Pokam, Gilles, Abu-Ghazaleh, Nael, Tullsen, Dean |
| Conference Name | 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) |
| Date Published | oct |
| Keywords | Adversarial Machine Learning Attacks, Automated Hardware Performance Counter Engineering, Automatic Attack Sample Generation, codes, composability, defense, Detectors, generative adversarial networks, Hardware, hardware security, Linearized Neural Network, Metrics, Microarchitectural Attack Detection, microarchitecture, ML Interpretability, pubcrawl, resilience, Resiliency, Side channel, side-channel attacks, Switches, Training, Zero Day Attack Defense, Zero day attacks |
| Abstract | This paper provides an end-to-end solution to defend against known microarchitectural attacks such as speculative execution attacks, fault-injection attacks, covert and side channel attacks, and unknown or evasive versions of these attacks. Current defenses are attack specific and can have unacceptably high performance overhead. We propose an approach that reduces the overhead of state-of-art defenses by over 95%, by applying defenses only when attacks are detected. Many current proposed mitigations are not practical for deployment; for example, InvisiSpec has 27% overhead and Fencing has 74% overhead while protecting against only Spectre attacks. Other mitigations carry similar performance penalties. We reduce the overhead for InvisiSpec to 1.26% and for Fencing to 3.45% offering performance and security for not only spectre attacks but other known transient attacks as well, including the dangerous class of LVI and Rowhammer attacks, as well as covering a large set of future evasive and zero-day attacks. Critical to our approach is an accurate detector that is not fooled by evasive attacks and that can generalize to novel zero-day attacks. We use a novel Generative framework, Evasion Vaccination (EVAX) for training ML models and engineering new security-centric performance counters. EVAX significantly increases sensitivity to detect and classify attacks in time for mitigation to be deployed with low false positives (4 FPs in every 1M instructions in our experiments). Such performance enables efficient and timely mitigations, enabling the processor to automatically switch between performance and security as needed. |
| DOI | 10.1109/MICRO56248.2022.00085 |
| Citation Key | ajorpaz_evax_2022 |
EVAX: Towards a Practical, Pro-active & Adaptive Architecture for High Performance & Security