Shared Resource Denial-of-Service Attacks on Multicore Platforms

With the increased adoption of complex machine learning algorithms across many different fields, powerful computing platforms have become necessary to meet their computational needs. Multicore platforms are a popular choice as they provide greater computing capabilities and can still meet different size, weight, and power (SWaP) constraints. However, contention for shared hardware resources between multiple cores remains a significant challenge that can lead to interference and unpredictable timing behaviors. Furthermore, this contention can be intentionally induced by malicious actors with the specific goals of delaying safety-critical tasks and jeopardizing system safety. This is done by performing Denial-of-Service (DoS) attacks that target shared resources such that the other cores in a system are unable to access them. When done properly, these shared resource DoS attacks can significantly impact performance and threaten system stability. For example, DoS attacks can cause >300X slowdown on the popular Raspberry Pi 3 embedded platform.

Motivated by the inherent risks posed by these DoS attacks, this dissertation presents investigations and evaluations of shared resource contention on multicore platforms, and the impacts it can have on the performance of real-time tasks. We propose various DoS attacks that each target different shared resources in the memory hierarchy with the goal of causing as much slowdown as possible. We show that each attack can inflict significant temporal slowdowns to victim tasks on target platforms by exploiting different hardware and software mechanisms. We then develop and analyze techniques for providing shared resource isolation and temporal performance guarantees for safety-critical tasks running on multicore platforms. In particular, we find that bandwidth throttling mechanisms are effective solutions against most DoS attacks and can protect the performance of real-time victim tasks.