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

Abstract

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.

An Unsupervised Detection Approach for Hardware Trojans

“…PL-HTD also suffers from certain drawbacks such as unsupervised techniques require large amount of data with good coverage in order to perform effectively. But on the whole, our TNR achieves a similar performance level as the fully supervised classification XGBoost [12]. Our outliers detection is useful in improving the circuit quality and the follow-up analysis accuracy.…”

“…The detection results of HT-free netlists are shown in Table 11. We compare the results with that of supervised learning XGBoost algorithom [12]. When PL-HTD was used to detect the three netlists of free-s15850, free-s35932 and free-s38417, TNR reached more than 99.80%, which can correctly judge the normal network, almost no misjudgment of the normal nets as Trojan nets.…”

“…Dong et al [11] proposed a locating method for multi-purposes HTs based on the boundary network to achieve the HTs' precise locations. Dong et al [12] proposed an XGBoost-based HTs detection method at the gate-level which obtained the best detection results among the existing supervised machinelearning-based HTs detection methods.…”

“…By analyzing the fan_in_x feature among the existing features, it can be found that the malicious attacker can easily alter the structure of the Trojan circuit to an equivalent one, thus changing the value of the fan_in_x feature. So the number of logic gates far away from x-level target net n is defined as in_gate_x except the 51 features [12]. The data provided by the Trust-HUB is the source file of the gate-level netlists, which cannot be directly used for the hardware Trojans classifier.…”

“…The post-silicon methods are not applicable when the IC is integrated highly. The pre-silicon methods are good choices, and with high integration, the machine-learning-based pre-silicon methods can express better performance [10]- [12]. The existing machine-learningbased HT detecting methods are all supervised.…”