Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.

In IoT (Internet-of-Things) era, the number and variety of hardware devices becomes continuously increasing. Several IoT devices are utilized at infrastructure equipments. How to maintain such IoT devices is a serious concern. Capacitance measurement is one of the powerful ways to detect anomalous states in the structure of the hardware devices. Particularly, measuring capacitance while the hardware device is running is a major challenge but no such researches proposed so far. This paper proposes a capacitance measuring device which measures device capacitance in operation. We firstly combine the AC (alternating current) voltage signal with the DC (direct current) supply voltage signal and generates the fluctuating signal. We supply the fluctuating signal to the target device instead of supplying the DC supply voltage. By effectively filtering the observed current in the target device, the filtered current can be proportional to the capacitance value and thus we can measure the target device capacitance even when it is running. We have implemented the proposed capacitance measuring device on the printed wiring board with the size of 95mm × 70mm and evaluated power consumption and accuracy of the capacitance measurement. The experimental results demonstrate that power consumption of the proposed capacitance measuring device is reduced by 65% in low-power mode from measuring mode and proposed device successfully measured capacitance in 0.002μF resolution.

It has been reported that malicious third-party IC vendors often insert hardware Trojans into their IC products. How to detect them is a critical concern in IC design process. Machine-learning-based hardware-Trojan detection gives a strong solution to tackle this problem. Hardware-Trojan infected nets (or Trojan nets) in ICs must have particular Trojan-net features, which differ from those of normal nets. In order to classify all the nets in a netlist designed by third-party vendors into Trojan nets and normal ones by machine learning, we have to extract effective Trojan-net features from Trojan nets. In this paper, we first propose 51 Trojan-net features which describe well Trojan nets. After that, we pick up random forest as one of the best candidates for machine learning and optimize it to apply to hardware-Trojan detection. Based on the importance values obtained from the optimized random forest classifier, we extract the best set of 11 Trojan-net features out of the 51 features which can effectively classify the nets into Trojan ones and normal ones, maximizing the F-measures. By using the 11 Trojan-net features extracted, our optimized random forest classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several Trust-HUB benchmarks and obtained the average F-measure of 79.3% and the accuracy of 99.2%, which realize the best values among existing machine-learning-based hardware-Trojan detection methods.

Due to the rapid growth of the information industry, various Internet of Things (IoT) devices have been widely used in our daily lives. Since the demand for low-cost and high-performance hardware devices has increased, malicious third-party vendors may insert malicious circuits into the products to degrade their performance or to leak secret information stored at the devices. The malicious circuit surreptitiously inserted into the hardware products is known as a ‘hardware Trojan.’ How to detect hardware Trojans becomes a significant concern in recent hardware production. In this paper, we propose a hardware Trojan detection method that employs two-stage neural networks and effectively utilizes the Trojan probability of neighbor nets. At the first stage, the 11 Trojan features are extracted from the nets in a given netlist, and then we estimate the Trojan probability that shows the probability of the Trojan nets. At the second stage, we learn the Trojan probability of the neighbor nets for each net in the netlist and classify the nets into a set of normal nets and Trojan ones. The experimental results demonstrate that the average true positive rate becomes 83.6%, and the average true negative rate becomes 96.5%, which is sufficiently high compared to the existing methods.

Due to the increase of outsourcing by IC vendors, we face a serious risk that malicious third-party vendors insert hardware Trojans very easily into their IC products. However, detecting hardware Trojans is very difficult because today's ICs are huge and complex. In this paper, we propose a hardware-Trojan classification method for gate-level netlists to identify hardware-Trojan infected nets (or Trojan nets) using a support vector machine (SVM) or a neural network (NN). At first, we extract the five hardware-Trojan features from each net in a netlist. These feature values are complicated so that we cannot give the simple and fixed threshold values to them. Hence we secondly represent them to be a five-dimensional vector and learn them by using SVM or NN. Finally, we can successfully classify all the nets in an unknown netlist into Trojan ones and normal ones based on the learned classifiers. We have applied our machine-learning-based hardware-Trojan classification method to Trust-HUB benchmarks. The results demonstrate that our method increases the true positive rate compared to the existing state-of-the-art results in most of the cases. In some cases, our method can achieve the true positive rate of 100%, which shows that all the Trojan nets in an unknown netlist are completely detected by our method.

Recently, it has been reported that malicious third-party IC vendors often insert hardware Trojans into their products. Especially in IC design step, malicious third-party vendors can easily insert hardware Trojans in their products and thus we have to detect them efficiently. In this paper, we propose a machine-learning-based hardware-Trojan detection method for gate-level netlists using multi-layer neural networks. First, we extract 11 Trojan-net feature values for each net in a netlist. After that, we classify the nets in an unknown netlist into a set of Trojan nets and that of normal nets using multi-layer neural networks. By experimentally optimizing the structure of multi-layer neural networks, we can obtain an average of 84.8% true positive rate and an average of 70.1% true negative rate while we can obtain 100% true positive rate in some of the benchmarks, which outperforms the existing methods in most of the cases.