A Hardware-Trojan Classification Method Using Machine Learning at Gate-Level Netlists Based on Trojan Features

Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu

doi:10.1587/transfun.e100.a.1427

Cited by 41 publications

(29 citation statements)

References 16 publications

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“…On the other hand, n features extracted from each net can be utilized to differentiate Trojan-infected nets from normal nets. For example, Kasegawa et al extracted five HT-net feature values from each net in gate-level netlists and learned them using a classifier such as SVM or ANNs [59]. After that, the trained classifier can be used to classify a set of features from an unknown gate-level netlist.…”

Section: -Reverse Engineeringmentioning

confidence: 99%

“…On the other hand, ML algorithms, especially supervised or unsupervised learning-based approaches, can be selected according to whether the training datasets are available. For example, the works of [59], [61], [73], and [86], among others, selected a classification-based approach for HT detection because they assumed that golden designs or ICs are available as training datasets (see Table 15).…”

Section: ) Reasonable Selection Of the Learning Modelsmentioning

confidence: 99%

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A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

et al. 2020

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The remarkable success of machine learning (ML) in a variety of research domains has inspired academic and industrial communities to explore its potential to address hardware Trojan (HT) attacks. While numerous works have been published over the past decade, few survey papers, to the best of our knowledge, have systematically reviewed the achievements and analyzed the remaining challenges in this area. To fill this gap, this article surveys ML-based approaches against HT attacks available in the literature. In particular, we first provide a classification of all possible HT attacks and then review recent developments from four perspectives, i.e., HT detection, design-for-security (DFS), bus security, and secure architecture. Based on the review, we further discuss the lessons learned in and challenges arising from previous studies. Despite current work focusing more on chip-layer HT problems, it is notable that novel HT threats are constantly emerging and have evolved beyond chips and to the component, device, and even behavior layers, therein compromising the security and trustworthiness of the overall hardware ecosystem. Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security threats and requirements in each layer to provide a guideline for future research in this direction.

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Section: -Reverse Engineeringmentioning

confidence: 99%

Section: ) Reasonable Selection Of the Learning Modelsmentioning

confidence: 99%

A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

et al. 2020

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“…Hasegawa et al [28] Dong et al [10] came up with a framework called RG-Secure. They used the lightGBM algorithm to detect gatelevel netlists from Trust-HUB [31].…”

Section: B Gate-level Netlists Detection Based On Machine Learningmentioning

confidence: 99%

An Unsupervised Detection Approach for Hardware Trojans

et al. 2020

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With the booming development of the cyber-physical system, human society is much more dependent on information technology. Unfortunately, like software, hardware is not trusted at all, due to so many third parties involved in the separated integrated circuit's (IC) design and manufacturing stages for the high profit. The malicious circuits (named Hardware Trojans) can be implanted during any stage of the ICs' design and manufacturing process. However, the existing pre-silicon approaches based on machine learning theory have good performance, they all belong to supervised learning methods, which have a key prerequisite that is numerous already known information. Meanwhile, hardware Trojans are even more unimaginable because today's ICs are becoming more complicated. The known information is even harder to gain. Furthermore, the training process for supervised learning methods tends to be time-consuming and generally requires a huge amount of balanced training data. Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD. We firstly visualize the distribution features of normal nets and Trojan nets, and then reveal the differences between the two types of nets to reduce the dimension of the feature set. According to the outliers of each net, the abnormal nets are selected and verified by professionals later to confirm whether it is a true Trojan relative to the host circuit to realize the detection. The experiments show that the proposed method can detect hardware Trojans effectively and reduce the cost of manual secondary detection. For the Trust-HUB benchmarks, the PL-HTD achieves up to 73.08% TPR and 97.52% average TNR, moreover, it achieves average 96.00% accuracy, which shows the feasibility and efficiency of hardware Trojans detecting by employing a method without the guidance of class label information. INDEX TERMS hardware security, hardware Trojan detection, integrated circuit, unsupervised machine learning, LOF

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“…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.…”

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

confidence: 99%

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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A Hardware-Trojan Classification Method Using Machine Learning at Gate-Level Netlists Based on Trojan Features

Cited by 41 publications

References 16 publications

A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

An Unsupervised Detection Approach for Hardware Trojans

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

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