SYSFID—System-Aware Fault-Injection Attack Detection for System-In-Package Architectures

Abstract A system-in-package (SiP) design takes advantage of cutting-edge packaging technology and heterogeneous integration (HI) in response to the growing need for aggressive time-to-market, high-performance, less expensive, and smaller systems. However, aggregating dies with different functionalities introduces new attack vectors with fault-injection attacks (FIA) that can effectively alter a circuit's data and control flow maliciously to cause disruptions of secure communication or sensitive information leakage. Additionally, traditional threat models associated with FIA on a 2D monolithic system-on-chip (SoC), and the corresponding mitigation techniques may not be compatible with modern 2.5D and 3D SiP architectures. To address these limitations, we propose system-aware fault injection attack detection for SiP architectures (SYSFID), a real-time and on-chip sensor-based fault monitoring approach integrated into a system-level design. SYSFID detects any fault-induced anomalous alterations in path delays of the components of inter-chiplet networks by strategically placing on-chip fault-to-time converter (FTC) sensors and controlling them efficiently to safeguard overall system security. To demonstrate the effectiveness of SYSFID, we detect several fault injection attempts on the FPGA implementation of a network-on-chip (NoC) based architecture during secure network packet transfers. Our experiments also illustrate that the SYSFID framework reliably senses both global and local FIAs with minimal overheads.

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Publication Details

Journal
Proceedings - International Symposium for Testing and Failure Analysis
Published
2024-10-23
DOI
https://doi.org/10.31399/asm.cp.istfa2024p0332
Citations
2
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
0.98
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article

SYSFID—System-Aware Fault-Injection Attack Detection for System-In-Package Architectures

Md Latifur Rahman, Amit Mazumder Shuvo, Farimah Farahmandi, Kimia Zamiri Azar et al.
2 citations
Proceedings - International Symposium for Testing and Failure Analysis
Physical Unclonable Functions (PUFs) and Hardware Security
0.98
article

SYSFID—System-Aware Fault-Injection Attack Detection for System-In-Package Architectures

Md Latifur Rahman, Amit Mazumder Shuvo, Farimah Farahmandi, Kimia Zamiri Azar, Md Sami Ul Islam Sami, Jingbo Zhou, Mark Tehranipoor
article en
2 citations

Abstract

Abstract A system-in-package (SiP) design takes advantage of cutting-edge packaging technology and heterogeneous integration (HI) in response to the growing need for aggressive time-to-market, high-performance, less expensive, and smaller systems. However, aggregating dies with different functionalities introduces new attack vectors with fault-injection attacks (FIA) that can effectively alter a circuit's data and control flow maliciously to cause disruptions of secure communication or sensitive information leakage. Additionally, traditional threat models associated with FIA on a 2D monolithic system-on-chip (SoC), and the corresponding mitigation techniques may not be compatible with modern 2.5D and 3D SiP architectures. To address these limitations, we propose system-aware fault injection attack detection for SiP architectures (SYSFID), a real-time and on-chip sensor-based fault monitoring approach integrated into a system-level design. SYSFID detects any fault-induced anomalous alterations in path delays of the components of inter-chiplet networks by strategically placing on-chip fault-to-time converter (FTC) sensors and controlling them efficiently to safeguard overall system security. To demonstrate the effectiveness of SYSFID, we detect several fault injection attempts on the FPGA implementation of a network-on-chip (NoC) based architecture during secure network packet transfers. Our experiments also illustrate that the SYSFID framework reliably senses both global and local FIAs with minimal overheads.

Proceedings - International Symposium for Testing and Failure AnalysisVol. 84918
University of Florida (US)
Openalex Percentile: Top 24%
Physical Unclonable Functions (PUFs) and Hardware Security
0.98
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