Ensemble-Learning-Based Hardware Trojans Detection Method by Detecting the Trigger Nets
Title | Ensemble-Learning-Based Hardware Trojans Detection Method by Detecting the Trigger Nets |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Wang, Yuze, Han, Tao, Han, Xiaoxia, Liu, Peng |
Conference Name | 2019 IEEE International Symposium on Circuits and Systems (ISCAS) |
Date Published | may |
ISBN Number | 978-1-7281-0397-6 |
Keywords | cyber physical systems, ensemble learning method, feature extraction, Hardware, hardware-Trojan detection method, IC design phase, IC systems, integrated circuit design, Integrated circuit modeling, learning (artificial intelligence), pubcrawl, resilience, Resiliency, security, supply chain security, suspicious Trigger nets, Training, Training data, Trigger-net features, Trojan circuits, trojan horse detection, Trojan horses, Trojan types |
Abstract | With the globalization of integrated circuit (IC) design and manufacturing, malicious third-party vendors can easily insert hardware Trojans into their intellect property (IP) cores during IC design phase, threatening the security of IC systems. It is strongly required to develop hardware-Trojan detection methods especially for the IC design phase. As the particularity of Trigger nets in Trojan circuits, in this paper, we propose an ensemble-learning-based hardware-Trojan detection method by detecting the Trigger nets at the gate level. We extract the Trigger-net features for each net from known netlists and use the ensemble learning method to train two detection models according to the Trojan types. The detection models are used to identify suspicious Trigger nets in an unknown detected netlist and give results of suspiciousness values for each detected net. By flagging the top n% suspicious nets of each detection model as the suspicious Trigger nets based on the suspiciousness values, the proposed method can achieve, on average, 88% true positive rate, 90% true negative rate, and 90% Accuracy. |
URL | https://ieeexplore.ieee.org/document/8702539 |
DOI | 10.1109/ISCAS.2019.8702539 |
Citation Key | wang_ensemble-learning-based_2019 |
- resilience
- Trojan types
- Trojan horses
- trojan horse detection
- Trojan circuits
- Trigger-net features
- Training data
- Training
- suspicious Trigger nets
- supply chain security
- security
- Resiliency
- cyber physical systems
- pubcrawl
- learning (artificial intelligence)
- Integrated circuit modeling
- integrated circuit design
- IC systems
- IC design phase
- hardware-Trojan detection method
- Hardware
- feature extraction
- ensemble learning method