SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation

Wang, Xinmu; Narasimhan, Seetharam; Krishna, Aswin; Bhunia, Swarup

doi:10.1109/vlsid.2012.88

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…A non-destructive side-channel analysis based reverse engineering (SCARE) was proposed by Wang et al [169], which could be used for both cases where golden design might or might not available. For trojan detections, they used side-channel to extract structural and functional information from the chip.…”

Section: Side-channel Based Trojan Detection Methodsmentioning

confidence: 99%

Physical attacks on block ciphers

Jap¹

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The security of a cryptosystem is often compromised, not from a theoretical point of view, but by the leakage caused by the physical implementation of the cryptographic algorithm. A new class of attacks, called physical attacks, has shown the capability to exploit the unintentional physical behaviors from the cryptographic device, which usually provide enough information to recover the secret keys. Dierent methods have been proposed for conducting the attacks. Two of the main focus of physical attacks are side-channel attacks and fault attacks. For side-channel attacks, the strongest cryptanalysis can be carried when the attacker can prole the targeted device. In proling based side-channel attacks, a model is constructed to characterize the leakage behavior from the device. Recently, machine learning algorithms have been proposed as alternatives for the classical proling based attacks. Machine learning and side-channel analysis are two dierent elds of study, however they are similar, in a sense that both are mostly dealing with the same problem (i.e., classication). For fault attacks, the aim of the attacker is to disrupt the execution of cryptographic algorithms. Based on the erroneous results, it is possible to gain some additional information regarding the secret key. Many methods can be used to force a fault to the device, however, laser fault injection is still considered as the preferred tools for injecting faults, due to its high precision and repeatability.

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Section: Side-channel Based Trojan Detection Methodsmentioning

confidence: 99%

Physical attacks on block ciphers

Jap¹

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Towards Micro-architectural Leakage Simulators: Reverse Engineering Micro-architectural Leakage Features Is Practical

Gao

Oswald

Page

2022

Advances in Cryptology – EUROCRYPT 2022

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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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SCARE: Side-Channel Analysis Based Reverse Engineering for Post-Silicon Validation

Cited by 6 publications

References 8 publications

Physical attacks on block ciphers

Physical attacks on block ciphers

Towards Micro-architectural Leakage Simulators: Reverse Engineering Micro-architectural Leakage Features Is Practical

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

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