Towards bidirectional LUT-level detection of hardware Trojans

Cho, Mingi; Jang, Jae-Dong; Seo, Yezee; Jeong, Seyeon; Chung, Soochang; Kwon, Taekyoung

doi:10.1016/j.cose.2021.102223

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…Continuous Intention: To assess the extent to which an individual intends to continue using WhatsApp, the threeitem continuous intention scale developed by Bhattacherjee [87][88][89] was used (e.g., "I intend to continue using WhatsApp").…”

Section: Methodology and Analysismentioning

confidence: 99%

Why Millennials Continue to Use WhatsApp? A Focus on Culture and Computer–Human Dialogue

Allil,

Faisal,

Zia

2024

Human Behavior and Emerging Technologies

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Continuous usage behavior among millennials is crucial, yet factors influencing this behavior are not well understood. This study integrates human-computer interaction and marketing research to investigate the relationship between persuasive dialogue support (PDS), attitudinal loyalty (AL), and continuous-use intention (CI) in the context of WhatsApp. The study also examines the moderating role of four cultural dimensions: uncertainty avoidance (UA), power distance (PD), masculinity (MAS), and individualism (IND) in these relationships. Data was collected from 287 WhatsApp users in four countries, and the proposed theoretical model was tested using structural equation modelling (SEM-PLS). The results indicate that PDS has a direct positive impact on AL, which in turn leads to CI. The study also found that cultural differences can moderate the relationship between PDS and AL and between AL and CI. Therefore, app developers need to consider cultural differences when designing their products and implement persuasive design aspects to boost user loyalty and intention to use. To ensure optimal app performance in diverse cultural settings, developers should strike an appropriate balance between universal design principles and culturally sensitive adaptation. However, the study’s limitations, such as reliance on self-reported data and a relatively small sample size, suggest the need for future research to expand its scope to include participants from a broader range of cultural backgrounds.

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Section: Methodology and Analysismentioning

confidence: 99%

Why Millennials Continue to Use WhatsApp? A Focus on Culture and Computer–Human Dialogue

Allil,

Faisal,

Zia

2024

Human Behavior and Emerging Technologies

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“…Cho et al. [ 49 ] proposed a bidirectional mechanism to detect hardware Trojans in FPGAs at any stage in order to efficiently detect them. Similarly, for FPGA research, Ma et al.…”

Section: Related Workmentioning

confidence: 99%

“…Alhelaly et al [48] proposed a method for detecting hardware Trojans in 3D integrated circuits and also explored the performance of this detection method when the attacker modified the characteristics of TSVs. Cho et al [49] proposed a bidirectional mechanism to detect hardware Trojans in FPGAs at any stage in order to efficiently detect them. Similarly, for FPGA research, Ma et al [50] proposed an on-chip security framework that can be used to verify whether an HT has tampered with or corrupted the original design.…”

Section: Detection and Prevention For Specific Chipsmentioning

confidence: 99%

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

Dong,

Yao,

et al. 2023

Sensors

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The Cyber-Physical System and even the Metaverse will become the second space in which human beings live. While bringing convenience to human beings, it also brings many security threats. These threats may come from software or hardware. There has been a lot of research on managing malware, and there are many mature commercial products, such as antivirus software, firewalls, etc. In stark contrast, the research community on governing malicious hardware is still in its infancy. Chips are the core component of hardware, and hardware Trojans are the primary and complex security issue faced by chips. Detection of hardware Trojans is the first step for dealing with malicious circuits. Due to the limitation of the golden chip and the computational consumption, the existing traditional detection methods are not applicable to very large-scale integration. The performances of traditional machine-learning-based methods depend on the accuracy of the multi-feature representation, and most of the methods may lead to instability because of the difficulty of extracting features manually. In this paper, employing deep learning, a multiscale detection model for automatic feature extraction is proposed. The model is called MHTtext and provides two strategies to balance the accuracy and computational consumption. After selecting a strategy according to the actual situations and requirements, the MHTtext generates the corresponding path sentences from the netlist and employs TextCNN for identification. Further, it can also obtain non-repeated hardware Trojan component information to improve its stability performance. Moreover, a new evaluation metric is established to intuitively measure the model’s effectiveness and balance: the stabilization efficiency index (SEI). In the experimental results for the benchmark netlists, the average accuracy (ACC) in the TextCNN of the global strategy is as high as 99.26%, and one of its stabilization efficiency index values ranks first with a score of 71.21 in all comparison classifiers. The local strategy also achieved an excellent effect, according to the SEI. The results show that the proposed MHTtext model has high stability, flexibility, and accuracy, in general.

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“…The experimental results provided showed that after the database creation, which can take up to 120 hours, the bitstream reverse engineering process takes less than a minute. In 2021, M. Cho et al [55] published yet another methodology that made improvements over the BIL's PIP reverse engineering strategy. Their experiments showed an accuracy of 92.9%, which is similar to techniques presented earlier, such as bit2NCD and BRET.…”

Section: Bitstream Reverse Engineeringmentioning

confidence: 99%

FPGA Bitstream Modification: Attacks and Countermeasures

Moraitis

2023

IEEE Access

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Advances in FPGA technology in recent years have resulted in an expansion of its usage in a very wide spectrum of applications. Apart from serving the traditional prototyping purposes, FPGAs are currently regarded as an integral part of embedded systems used in many industries, including communication, medical, aerospace, automotive, and military. Moreover, the emerging trend of AI has found FPGAs to be at the technological forefront with their use as deep learning acceleration platforms. The demand for FPGAs has grown to the point that major companies (e.g. Amazon) are offering cloudbased access to FPGAs, known as FPGA-as-a-Service. In many applications, FPGAs handle sensitive data and/or host cryptographic algorithm implementations. These FPGAs are not always located in a tamperresistant environment, which makes their security a major concern, especially in light of the ever-growing number of publications demonstrating effective attacks specifically tailored to exploit the physical traits of FPGA implementations. In this survey, we cover the subset of those attacks that involve tampering with the FPGA configuration bitstream. We start by discussing how the FPGA vendors attempt to protect their products and how malicious parties try to overcome this protection. We then proceed to present the different bitstream modification attacks that can be found in the literature organized according to their targets. Finally, we present various countermeasures that can be deployed, drawing on bibliographic references from works specifically focused on FPGA bitstream protection, as well as those initially proposed for different purposes or devices that can be adapted for bitstream protection.

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Towards bidirectional LUT-level detection of hardware Trojans

Cited by 8 publications

References 10 publications

Why Millennials Continue to Use WhatsApp? A Focus on Culture and Computer–Human Dialogue

Why Millennials Continue to Use WhatsApp? A Focus on Culture and Computer–Human Dialogue

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

FPGA Bitstream Modification: Attacks and Countermeasures

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