Balancing Security and Efficiency: System-Informed Mitigation of Power-Based Covert Channels

As the digital landscape continues to evolve, the security of computing systems has become a critical concern. Power-based covert channels (e.g., thermal covert channel s (TCCs)), a form of communication that exploits the system resources to transmit information in a hidden or unintended manner, hav...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems 2024-11, Vol.43 (11), p.3395-3406
Main Authors: Gonzalez-Gomez, Jeferson, Sikal, Mohammed Bakr, Khdr, Heba, Bauer, Lars, Henkel, Jorg
Format: Article
Language:English
Subjects:
Channels
Commercial aircraft
Countermeasures
covert channels
Design automation
Embedded systems
Energy efficiency
Frequency modulation
Integrated circuits
Machine learning
machine learning (ML)
Prevention and mitigation
Security
security threats
Voltage
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As the digital landscape continues to evolve, the security of computing systems has become a critical concern. Power-based covert channels (e.g., thermal covert channel s (TCCs)), a form of communication that exploits the system resources to transmit information in a hidden or unintended manner, have been recently studied as an effective mechanism to leak information between malicious entities via the modulation of CPU power. To this end, dynamic voltage and frequency scaling (DVFS) has been widely used as a countermeasure to mitigate TCCs by directly affecting the communication between the actors. Although this technique has proven effective in neutralizing such attacks, it introduces significant performance and energy penalties, that are particularly detrimental to energy-constrained embedded systems. In this article, we propose different system-informed countermeasures to power-based covert channels from the heuristic and machine learning (ML) domains. Our proposed techniques leverage task migration and DVFS to jointly mitigate the channels and maximize energy efficiency. Our extensive experimental evaluation on two commercial platforms: 1) the NVIDIA Jetson TX2 and 2) Jetson Orin shows that our approach significantly improves the overall energy efficiency of the system compared to the state-of-the-art solution while nullifying the attack at all times.
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2024.3438999