Survey of Hardware Trojan Threats and Detection

Hayashi, Yu-ichi; Kawamura, S

doi:10.1109/emceurope48519.2020.9245675

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…In the process of designing, manufacturing, and encapsulating IC chips, there have been active discussions in recent years about the "possibility of intentional addition of circuits and devices or tampering with circuits and layouts" that can be used by malicious attackers to insert backdoor functions to access confidential information, leak that information, or damage the equipment itself [31].…”

Section: A Threats To Confidentiality Integrity and Availabilitymentioning

confidence: 99%

Introduction to Physical Layer Security and Hardware Supply Chain Security: EM Tricks to Keep Your Information and Devices Safe

Hayashi,

Leferink,

Nagata

2023

2023 International Symposium on Electromagnetic Compatibility – EMC Europe

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This paper focuses on the importance of ensuring hardware security to protect personal privacy, e-commerce, and data integrity in the current information society. In particular, we will focus on electromagnetic information security threats that leave few traces of attacks among hardware security threats, as well as security threats and countermeasures related to the hardware supply chain, which has been covered as a new threat in recent years. The need for offensive security measures to anticipate threats, as well as resilience functions to acquire resistance to threats after an incident, is also discussed. The paper suggests that training engineers and researchers with knowledge of both EMC and security is essential for future hardware designs.

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Section: A Threats To Confidentiality Integrity and Availabilitymentioning

confidence: 99%

Introduction to Physical Layer Security and Hardware Supply Chain Security: EM Tricks to Keep Your Information and Devices Safe

Hayashi,

Leferink,

Nagata

2023

2023 International Symposium on Electromagnetic Compatibility – EMC Europe

Self Cite

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“…There are emerging threats such as Hardware Trojans (HT) among modern electronics systems [35]. Crypto IC chips are unexpectedly made more vulnerable by HTs through malicious actions in diversified ways, where SC leakage properties such as power, delay, and EM waves are multiply explored [36]. The SC leakages have been challenged by HT circuits which are located within an IC chip [37] or even on a board [38], while also utilized for HT detection within an IC chip in the operation field [39] as well as during product testing [40].…”

Section: A General Descriptionmentioning

confidence: 99%

Physical Attack Protection Techniques for IC Chip Level Hardware Security

Nagata

Miki

Miura

2022

IEEE Trans. VLSI Syst.

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Hardware Trojan Detection Method Based on Dual Discriminator Assisted Conditional Generation Adversarial Network

Tang¹,

Su²,

Gao³

2023

J Electron Test

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Hardware Trojans are usually implanted by making malicious changes to a chip circuit, which can destroy chip functions or expose sensitive information once activated. The hardware Trojan detection method based on side channel information has now become one of the most widely used detection methods. However, due to the influence of the deviation of the acquisition equipment and the noise of the actual chip working environment, insufficient acquisition of useful information of the collected side channel information occurs, affecting the final results. To address the problem, this paper proposes a detection method based on a dual discriminator assisted conditional generation adversarial network (D2ACGAN), which combines the benefits of CGAN, ACGAN, and D2GAN models and can learn a variety of valid information of the tested chip. It can distinguish between side channel data with and without hardware Trojan and classify hardware Trojan using the extended data. Furthermore, to compare the performance of the proposed model, we use the existing CGAN and ACGAN models equally for side channel information expansion and hardware Trojan detection. Finally, the designed hardware Trojan is implanted in an encryption chip for generating data quality evaluation experiments and model method performance experiments. The results show that the average detection accuracy of the D2ACGAN-based hardware Trojan classification model can reach 97.08%, which is better than the detection models based on CNN, SVM, etc. The D2ACGAN model also outperforms the CGAN and ACGAN models in terms of generated data and hardware Trojan classification.

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Survey of Hardware Trojan Threats and Detection

Cited by 9 publications

References 39 publications

Introduction to Physical Layer Security and Hardware Supply Chain Security: EM Tricks to Keep Your Information and Devices Safe

Introduction to Physical Layer Security and Hardware Supply Chain Security: EM Tricks to Keep Your Information and Devices Safe

Physical Attack Protection Techniques for IC Chip Level Hardware Security

Hardware Trojan Detection Method Based on Dual Discriminator Assisted Conditional Generation Adversarial Network

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