| Title | Run-time Detection and Mitigation of Power-Noise Viruses |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Tenentes, Vasileios, Das, Shidhartha, Rossi, Daniele, Al-Hashimi, Bashir M. |
| Conference Name | 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS) |
| Keywords | Arm multicore processor, Benchmark testing, computer viruses, data corruptions, Metrics, microcontrollers, Microprocessors, multicore computing security, multicore microprocessors, multiprocessing systems, operating frequency, power aware computing, power viruses, power-noise attacks, power-noise virus detection, power-noise virus mitigation, pubcrawl, regression analysis, Resiliency, resonance detection, Resonant frequency, run-time estimation, run-time system, Scalability, security, system crashes, system-on-chip, Threshold voltage, Viruses (medical), voltage emergencies, voltage noise data |
| Abstract | Power-noise viruses can be used as denial-of-service attacks by causing voltage emergencies in multi-core microprocessors that may lead to data corruptions and system crashes. In this paper, we present a run-time system for detecting and mitigating power-noise viruses. We present voltage noise data from a power-noise virus and benchmarks collected from an Arm multi-core processor, and we observe that the frequency of voltage emergencies is dramatically increasing during the execution of power-noise attacks. Based on this observation, we propose a regression model that allows for a run-time estimation of the severity of voltage emergencies by monitoring the frequency of voltage emergencies and the operating frequency of the microprocessor. For mitigating the problem, during the execution of critical tasks that require protection, we propose a system which periodically evaluates the severity of voltage emergencies and adapts its operating frequency in order to honour a predefined severity constraint. We demonstrate the efficacy of the proposed run-time system. |
| DOI | 10.1109/IOLTS.2019.8854375 |
| Citation Key | tenentes_run-time_2019 |
Run-time Detection and Mitigation of Power-Noise Viruses