King's College London

Research portal

Run-time Detection and Mitigation of Power-Noise Viruses

Research output: Contribution to conference typesPaper

Standard

Run-time Detection and Mitigation of Power-Noise Viruses. / Tenentes, V.; Das, S.; Rossi, D.; Al-Hashimi, Bashir M.

2019. 275-280.

Research output: Contribution to conference typesPaper

Harvard

Tenentes, V, Das, S, Rossi, D & Al-Hashimi, BM 2019, 'Run-time Detection and Mitigation of Power-Noise Viruses', pp. 275-280. https://doi.org/10.1109/IOLTS.2019.8854375

APA

Tenentes, V., Das, S., Rossi, D., & Al-Hashimi, B. M. (2019). Run-time Detection and Mitigation of Power-Noise Viruses. 275-280. https://doi.org/10.1109/IOLTS.2019.8854375

Vancouver

Tenentes V, Das S, Rossi D, Al-Hashimi BM. Run-time Detection and Mitigation of Power-Noise Viruses. 2019. https://doi.org/10.1109/IOLTS.2019.8854375

Author

Tenentes, V. ; Das, S. ; Rossi, D. ; Al-Hashimi, Bashir M. / Run-time Detection and Mitigation of Power-Noise Viruses.

Bibtex Download

@conference{83b1485b4b694ce39c758b1cd20738a8,
title = "Run-time Detection and Mitigation of Power-Noise Viruses",
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.",
author = "V. Tenentes and S. Das and D. Rossi and Al-Hashimi, {Bashir M.}",
note = "cited By 0",
year = "2019",
month = oct,
day = "3",
doi = "10.1109/IOLTS.2019.8854375",
language = "English",
pages = "275--280",

}

RIS (suitable for import to EndNote) Download

TY - CONF

T1 - Run-time Detection and Mitigation of Power-Noise Viruses

AU - Tenentes, V.

AU - Das, S.

AU - Rossi, D.

AU - Al-Hashimi, Bashir M.

N1 - cited By 0

PY - 2019/10/3

Y1 - 2019/10/3

N2 - 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.

AB - 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.

U2 - 10.1109/IOLTS.2019.8854375

DO - 10.1109/IOLTS.2019.8854375

M3 - Paper

SP - 275

EP - 280

ER -

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454