| Title | Blacklist Core: Machine-Learning Based Dynamic Operating-Performance-Point Blacklisting for Mitigating Power-Management Security Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Zhang, Sheng, Tang, Adrian, Jiang, Zhewei, Sethumadhavan, Simha, Seok, Mingoo |
| Conference Name | Proceedings of the International Symposium on Low Power Electronics and Design |
| Publisher | ACM |
| ISBN Number | 978-1-4503-5704-3 |
| Keywords | Blacklist, Metrics, operating performance point, Power Management, pubcrawl, resilience, Resiliency, Scalability, security, Time Frequency Analysis |
| Abstract | Most modern computing devices make available fine-grained control of operating frequency and voltage for power management. These interfaces, as demonstrated by recent attacks, open up a new class of software fault injection attacks that compromise security on commodity devices. CLKSCREW, a recently-published attack that stretches the frequency of devices beyond their operational limits to induce faults, is one such attack. Statically and permanently limiting frequency and voltage modulation space, i.e., guard-banding, could mitigate such attacks but it incurs large performance degradation and long testing time. Instead, in this paper, we propose a run-time technique which dynamically blacklists unsafe operating performance points using a neural-net model. The model is first trained offline in the design time and then subsequently adjusted at run-time by inspecting a selected set of features such as power management control registers, timing-error signals, and core temperature. We designed the algorithm and hardware, titled a BlackList (BL) core, which is capable of detecting and mitigating such power management-based security attack at high accuracy. The BL core incurs a reasonably small amount of overhead in power, delay, and area. |
| DOI | 10.1145/3218603.3218624 |
| Citation Key | zhang_blacklist_2018 |
Blacklist Core: Machine-Learning Based Dynamic Operating-Performance-Point Blacklisting for Mitigating Power-Management Security Attacks