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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The design complexity and outsourcing trend of modern integrated circuits (ICs) have increased the chance for adversaries to implant hardware Trojans (HTs) in the development process. To effectively defend against this hardware-based security threat, many solutions have been reported in the literature, including dynamic and static techniques. However, there is still a lack of methods that can simultaneously detect and diagnose HT circuits with high accuracy and low time complexity. Therefore, to overcome these limitations, this paper presents an HT detection and diagnosis method for gate-level netlists (GLNs) based on different machine learning (ML) algorithms. Given a GLN, the proposed method first partitions it into several circuit cones and extracts seven HT-related features from each cone. Then, we repeat this process for the sample GLN to construct a dataset for the next step. After that, we use K-Nearest Neighbor (KNN), Decision Tree (DT) and Naive Bayes (NB) to classify all circuit cones of the target GLN. Finally, we determine whether each circuit cone is HT-implanted through the label, completing the HT detection and diagnosis for target GLN. We have applied our method to 11 GLNs from ISCAS’85 and ISCAS’89 benchmark suites. As shown in experimental results of the three ML algorithms used in our method: (1) NB costs shortest time and achieves the highest average true positive rate (ATPR) of 100%; (2) DT costs longest time but achieve the highest average true negative rate (ATNR) of 98.61%; (3) Compared to NB and DT, KNN costs a slightly longer time than NB but the ATPR and ATNR values are approximately close to DT. Moreover, it can also report the possible implantation location of a Trojan instance according to the detecting results.

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.