A hybrid solution for constraint devices to detect microarchitectural attacks
Résumé
We are seeing an increase in cybersecurity attackson resource-constrained systems such as the Internet ofThings (IoT) and Industrial IoT (IIoT) devices. Recently,a new category of attacks has emerged called microarchi-tectural attacks. It targets hardware units of the systemssuch as processor or memory and is often complicated ifnot impossible to remediate since it imposes modifying thehardware. In default to remediation, some solutions proposeto detect these attacks. Yet, most of them are not suitablefor embedded systems since they are based on a complexmachine learning algorithms.In this paper, we propose a security solution for edge-computing attack detection that uses a local-remote machinelearning implementation to find an equilibrium betweenaccuracy and decision-making latency while addressing thememory, performance, and communication bandwidth con-straints of resource-constrained systems. We demonstrateeffectiveness in the detection of multiple microarchitecturalattacks such as Rowhammer or cache attacks on an embed-ded device with an accuracy of 98.75% and a FPR near 0%. To limit the overhead on the communication bus, theproposed solution filters 99% of the samples during normaloperation.
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