Hardware Trojan: Malware Detection Using Reverse Engineering and SVM

Jain, Girishma; Raghuwanshi, Sandeep K.; Vishwakarma, Gagan

doi:10.1007/978-3-319-76348-4_51

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…Jain et al [ 114 ] presented an HT detection method that combines reverse engineering (RE) and SVM algorithms. This means it can avoid damage during common RE inspections.…”

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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“…Jain et al [ 114 ] presented an HT detection method that combines reverse engineering (RE) and SVM algorithms. This means it can avoid damage during common RE inspections.…”

Section: Hardware Trojans Protectionmentioning

confidence: 99%

Hardware Trojans in Chips: A Survey for Detection and Prevention

Dong

Liu

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Аналіз останніх досліджень і публікацій. Дослідження апаратних троянських програм на основі реверс-інжинірингу виконано в [3]. За його основу взято метод опорних векторів.…”

unclassified

Functional model of reverse engineering malware

Tsurkan¹,

Voloshin²

2021

ITS

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SECURE: A Segmentation Quality Evaluation Metric on SEM Images for Reverse Engineering on Integrated Circuits

Wilson,

Dizon-Paradis,

Forte

et al. 2023

IEEE Access

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Comprehensive hardware assurance and failure analysis methods require exhaustive validation of the design layout through post-silicon imaging. Overlooked segmentation errors in such images cause significant inflation of resource requirements, both in terms of human resources and process time frame, for fault isolation and debugging in the hardware assurance process. Further, due to their lack of contextual awareness, the existing segmentation measures lack the ability to detect and suppress segmentation errors. In this paper, we introduce the first reference-less context-aware segmentation quality evaluation metric for scanning electron microscopy images, called Structural Equivalency and Connection Uniformity for Reverse Engineering (SECURE), to alleviate this issue by capturing an implicit understanding of design layout from weakly labeled unpaired images. The proposed metric can be seamlessly integrated into the design layout validation workflow for in-line rejection or correction of corrupted segmented images preventing the corrupted data from compromising the reliability of the hardware assurance process while simultaneously supporting process automation. Exhaustive qualitative and quantitative validation is provided to support the efficacy of the metric.

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Hardware Trojan: Malware Detection Using Reverse Engineering and SVM

Cited by 4 publications

References 11 publications

Hardware Trojans in Chips: A Survey for Detection and Prevention

Hardware Trojans in Chips: A Survey for Detection and Prevention

Functional model of reverse engineering malware

SECURE: A Segmentation Quality Evaluation Metric on SEM Images for Reverse Engineering on Integrated Circuits

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