HTtext: A TextCNN-based pre-silicon detection for hardware Trojans

Xu, Yi; Chen, Zhenyi; Huang, Binhong; Liu, Ximeng; Dong, Chen

doi:10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00022

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Its common realization pattern keeps the model from being complicated. A brief description of the global strategy can be found in [ 31 ]. This section shows the details of the global strategy and proposes a new local strategy and balancing scheme.…”

Section: Dataset Generationmentioning

confidence: 99%

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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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Section: Dataset Generationmentioning

confidence: 99%

“…The introduction of deep learning (DL) enables the automatic extraction of netlist features and results in a better generalization of the model. To address the above purpose, we proposed a DL-based approach for hardware Trojan detection to automatically extract features to identify and have a certain degree of promotion [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

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

Dong,

Yao,

et al. 2023

Sensors

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“…Lai et al 38 used TextCNN as the feature extractor to predict stock trends and achieved high improvement in the prediction accuracy. Besides, Xu et al 39 found that the TextCNN model is effective for detecting the hardware Trojans. In this study, we use TextCNN to extract the features of CEMs and classify the errors into different categories.…”

Section: Introductionmentioning

confidence: 99%

A Token‐based Compilation Error Categorization and Its Applications

Wang

Liu

et al. 2022

J Software Evolu Process

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Compilation errors are unavoidable during the debugging process of novice students. Compiler error messages can help novices to localize and remove errors, but these messages are difficult to understand for students. Previous studies have investigated the compilation error categorization by analyzing compiler error messages, but the categorization cannot cover all kinds of errors, which limits the evaluation of compilation error studies. Therefore, a comprehensive categorization for compilation errors is needed for evaluating the performance of models or tools related to compilation errors. In this study, we first propose a new compilation error categorization, which is based on the smallest unit of the program, tokens. The experiments on 29,573 programs from three datasets show that our proposed compilation error categorization can cover more types of errors and the distribution of the error categorization are significantly different between the datasets. Then, based on our proposed categorization, we develop a neural network model CLACER (CLAssification of Compilation ERrors) for predicting the compilation errors. The results indicate that CLACER can improve the compiler's error localization accuracy and predicts the compilation error effectively. Moreover, based on the proposed categorization, we conduct empirical studies to evaluate the performance of three repairing tools (i.e., DeepFix, RLAssist, and MACER). The comparison results illustrate that DeepFix and RLAssist can fix more errors in the category of delimiter than errors in other categories. Furthermore, MACER performs better than DeepFix and RLAssist because it has a sufficient repairing pattern set for the errors. We also provide some suggestions for improving the repairing tools in the future.

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