Hardware Trojans classification for gate-level netlists using multi-layer neural networks

Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu

doi:10.1109/iolts.2017.8046227

Cited by 102 publications

(93 citation statements)

References 10 publications

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“…25 First, compare the experimental results of Recall indicators, as shown in Figure 9. We can see that the RS232-T1100, RS232-T1200, RS232-T1300, RS232-T1400, RS232-T1500, and s35932-T100 netlists use the XGBoost-51 method, which is better than the MNN-11 25 detection effect in the Recall indicators. Although the average Recall of XGBoost-51 is lower than that of MNN-11, 25 it is also a good result.…”

Section: Comparison Of Experimental Resultsmentioning

confidence: 99%

“…Experimental results of existing machine-learningbased hardware-Trojan detection methods So far, some existing machine-learning hardware-Trojan detection methods are as follows: support vector machine (SVM)-based hardware-Trojan detection method, 22 neural network (NN)-based hardware-Trojan detection method, 23 random forest (RF)-based hardware-Trojan detection method, 24 and multi-layer neural network (MNN)-based hardware-Trojan detection method. 25 The true positive rate (TPR) and the true negative rate (TNR) are used by Hasegawa et al 22 and Inoue et al 26 to evaluate the detection results. In addition to the TPR and the TNR, Hasegawa et al 24 also used the Accuracy, the Precision, and the F-measure to evaluate the detection results and proposed that the F-measure is the best to measure the results very well.…”

Section: Hardware-trojan Detection Process For Gate-level Netlistmentioning

confidence: 99%

“…Hasegawa et al 24 used the RF classifier with 11 Trojan-net features to classify the normal nets and the Trojan nets, and obtained 94.9% the average Precision, 79.3% the average F-measure, and 99.2% the average Accuracy. Hasegawa et al 25…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

Dong

Chen

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall, 86.75% average F-measure, and 99.83% average Accuracy, which is the best detection result among existing machine-learning-based hardware-Trojan detection methods.

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Section: Comparison Of Experimental Resultsmentioning

confidence: 99%

Section: Hardware-trojan Detection Process For Gate-level Netlistmentioning

confidence: 99%

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A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

Dong

Chen

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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“…The Trojan features from the Trojan nets are extracted in [12] and a random forest classifier is applied to obtain the optimal set of Trojan features from the nets. Higher computational power is its limitations and requires more resources, since the model involves a lots of tree structure Classification of Hardware Trojan using multilayer neural network is discussed in [13]. The processing time is more to train the dataset for decision making which is its limitations since the hidden layers are based on feature set.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

Murugesan¹,

Manickam²

2020

IJIES

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In modern electronic design, hardware Trojan has emerged as a major threat in the hardware security. To detect the hardware Trojan is a major problem in testing process because of their inherent concealed nature. In this work, we propose a deep learning-based Trojan classification approach, which extracts the optimal feature to indicate the nets affected by the Trojan module. In this approach, a handcrafted algorithm along with the structural report is also analyzed for extracting further features of the gate level netlist, which stamp out the requirement of golden chip. This detection technique is also validated using game theoretical approach, which is modelled as zero-sum game between the attacker and the defender. The Simulation is employed on ISCAS’85, ISCAS’89 and Trust-HUB circuits and the deep learning algorithm performs the best in detection and classification of Trojan type with an average True positive rate of 96.69% and an accuracy of 96.25%.

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“…To defeat HTs and prevent HT-infected chips from being supplied to the market, researchers have proposed various HT detection techniques over the past decade [1], [2]. Benefiting from the development of training algorithms and computational power, very recent research shows a new trend in adopting machine learning (ML) approaches for HT detection [3], [4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Automatic Hardware Trojan Generation Platform

Liu²,

O’Neill

2019

2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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Over the past 10 years, various Hardware Trojan (HT) detection techniques have been proposed by the research community. However, the development of HT benchmark suites for testing and evaluating HT detection techniques lags behind. The number of HT-infected circuits available in current public HT benchmarks is somewhat limited and the circuits lack diversity in structure. Therefore, this paper proposes a new method to generate HTs using a highly configurable generation platform based on transition probability. The generation platform is highly configurable in terms of the HT trigger condition, trigger type, payload type and in the number and variety of HT-infected circuits that can be generated. In this research the transition probability of netlists is employed to identify rarely activated internal nodes to target for HT insertion rather than functional simulation as utilised in previous research. The authors believe transition probability provides a more realistic reflection of the netlist activity for use in determining the appropriate position for HT insertion. Finally, the generated HT-infected circuits are tested by a machine learning (ML)-based HT detection technique, which is known as Controllability and Observability for HT Detection (COTD). The resulting false positive and false negative rates illustrate the feasibility of the benchmark suite.

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Hardware Trojans classification for gate-level netlists using multi-layer neural networks

Cited by 102 publications

References 10 publications

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

An Improved Automatic Hardware Trojan Generation Platform

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