A Side-Channel Analysis for Hardware Trojan Detection Based on Path Delay Measurement

Amelian, Atieh; Borujeni, Shahram Etemadi

doi:10.1142/s0218126618501384

Cited by 34 publications

(9 citation statements)

References 6 publications

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“…The main advantage of the SCA approach is its ability to detect HTs even when HTs are not activated. The larger the HT, the more effective this method is and the simpler testing process [4][5]. Currently, SCA is considered among the most efficient techniques for HT detection [5].…”

Section: Existing Techniques For Hardware Trojan Detectionmentioning

confidence: 99%

Hardware Trojan Detection Based on Side-Channel Analysis Using Power Traces and Machine Learning

Hoang¹

2022

Proceedings of the Sixth International Conference on Research in Intelligent and Computing

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With the continuous development of the Integrated Circuit (IC) manufacturing where international outsourcing is one of the main trends, hardware Trojan (HT) has been considered as a serious problem for hardware security in modern electronic systems. This paper presents a novel HT detection method based on the side-channel analysis with power traces and the machine learning (ML) technique. Side-channel information of the AES encryption core was acquired by the power consumption measurement equipment and then classified with Softmax regression. The ML technique was applied to classify and detect the HT. The experimental results have clarified the efficiency of the proposed method.

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Section: Existing Techniques For Hardware Trojan Detectionmentioning

confidence: 99%

Hardware Trojan Detection Based on Side-Channel Analysis Using Power Traces and Machine Learning

Hoang¹

2022

Proceedings of the Sixth International Conference on Research in Intelligent and Computing

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“…Further, conclusive testing by performing a side channel analysis on the true positive trojan boards can improve the coverage of detecting hardware trojans. (Amelian and Borujeni 2018). This will help reduce the redundancy of testing all the boards for hardware trojans.…”

Section: Improvement In Recognition Of Hardware Trojansmentioning

confidence: 99%

A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

Kulkarni

2021

Front. Mech. Eng.

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Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipment. Security compromise or data theft can have severe impact and thus demand research attention. The advantage of the proposed method is that it can be implemented in a manufacturing environment with limited training data. It can also provide better coverage in detection of hardware trojans over traditional methods. Image recognition algorithms need to have deeper penetration inside the training layers for recognizing physical variations of image patches. However, traditional network architectures often face vanishing gradient problem when the network layers are added. This hampers the overall accuracy of the network. To solve this a Residual network with multiple layers is used in this article. The ResNet34 algorithm can identify manufacturing tolerances and can differentiate between a manufacturing defect and a hardware trojan. The ResNet operates on the fundamental principle of learning from the residual of the output of preceding layer. In the degradation issue, it is observed that, a shallower network performs better than deeper network. However, this is with the downside of lower accuracy. Thus, a skip connection is made to provide an alternative path for the gradient to skip forward the training of few layers and add in multiple repeating blocks to achieve higher accuracy and lower training times. Implementation of this method can bolster automated optical inspection setup used to detect manufacturing variances on a printed circuit board. The results show a 98.5% accuracy in optically detecting trojans by this method and can help cut down redundancy of physically testing each board. The research results also provide a new consideration of hardware trojan benchmarking and its effect on optical detection.

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“…Amelian et al [ 130 ] suggested a new kind of side-channel signal detection technique that doesn’t modify the circuit design and the hardware overhead. Moreover, the common side-channel signal detection techniques in VLSI have an alternative path to test.…”

Section: Hardware Trojans Protectionmentioning

confidence: 99%

Hardware Trojans in Chips: A Survey for Detection and Prevention

Dong

Liu

et al. 2020

Sensors

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Diverse and wide-range applications of integrated circuits (ICs) and the development of Cyber Physical System (CPS), more and more third-party manufacturers are involved in the manufacturing of ICs. Unfortunately, like software, hardware can also be subjected to malicious attacks. Untrusted outsourced manufacturing tools and intellectual property (IP) cores may bring enormous risks from highly integrated. Attributed to this manufacturing model, the malicious circuits (known as Hardware Trojans, HTs) can be implanted during the most designing and manufacturing stages of the ICs, causing a change of functionality, leakage of information, even a denial of services (DoS), and so on. In this paper, a survey of HTs is presented, which shows the threatens of chips, and the state-of-the-art preventing and detecting techniques. Starting from the introduction of HT structures, the recent researches in the academic community about HTs is compiled and comprehensive classification of HTs is proposed. The state-of-the-art HT protection techniques with their advantages and disadvantages are further analyzed. Finally, the development trends in hardware security are highlighted.

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A Side-Channel Analysis for Hardware Trojan Detection Based on Path Delay Measurement

Cited by 34 publications

References 6 publications

Hardware Trojan Detection Based on Side-Channel Analysis Using Power Traces and Machine Learning

Hardware Trojan Detection Based on Side-Channel Analysis Using Power Traces and Machine Learning

A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

Hardware Trojans in Chips: A Survey for Detection and Prevention

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