Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144232
Title: Mitigation of tampering attacks for MR-based thermal sensing in optical NoCs
Authors: Zhou, Jun
Li, Mengquan
Guo, Pengxing
Liu, Weichen
Keywords: Engineering::Computer science and engineering::Hardware::Input/output and data communications
Issue Date: 2020
Source: Zhou, J., Li, M., Guo, P., & Liu, W. (2020). Mitigation of tampering attacks for MR-based thermal sensing in optical NoCs. 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). doi:10.1109/ISVLSI49217.2020.00009
Abstract: As an emerging role in on-chip communication, the optical networks-on-chip (ONoCs) can provide ultra-high bandwidth, low latency and low power dissipation for the data transfer. However, the thermo-optic effects of the photonic devices have a great impact on the operating performance and reliability of ONoCs, where the thermal-aware control is used to alleviate it. Furthermore, the temperature-sensitive ONoCs are prone to be attacked by the hardware Trojans (HTs) covertly embedded in the integrated circuits (ICs) from the malicious third-party components, leading to performance degradation, denial of service (DoS), or even permanent damages. In this paper, we focus on the tampering attacks on optical sampling during the thermal sensing process in ONoCs. Corresponding approaches are proposed to mitigate the negative impacts from HT attacks. Evaluation results indicate that our approach can significantly enhance the hardware security of thermal sensing for ONoC with trivial overheads of up to 3.06% and 2.6% in average latency and energy consumption, respectively.
URI: https://hdl.handle.net/10356/144232
DOI: 10.1109/ISVLSI49217.2020.00009
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ISVLSI49217.2020.00009
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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