Abstract
Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 |
| Publisher | IEEE Computer Society |
| Volume | 2018-September |
| ISBN (Electronic) | 9781538660959 |
| DOIs | |
| Publication status | Published - 2018 Dec 13 |
| Event | 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany Duration: 2018 Sep 2 → 2018 Sep 5 |
Other
| Other | 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 |
|---|---|
| Country | Germany |
| City | Berlin |
| Period | 18/9/2 → 18/9/5 |
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Keywords
- design time
- gate-level netlist
- hardware Trojan
- machine learning
- neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Media Technology
Cite this
Designing subspecies of hardware trojans and their detection using neural network approach. / Inoue, Tomotaka; Hasegawa, Kento; Kobayashi, Yuki; Yanagisawa, Masao; Togawa, Nozomu.
2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. Vol. 2018-September IEEE Computer Society, 2018. 8576247.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Designing subspecies of hardware trojans and their detection using neural network approach
AU - Inoue, Tomotaka
AU - Hasegawa, Kento
AU - Kobayashi, Yuki
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
AB - Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
KW - design time
KW - gate-level netlist
KW - hardware Trojan
KW - machine learning
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85060285069&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060285069&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Berlin.2018.8576247
DO - 10.1109/ICCE-Berlin.2018.8576247
M3 - Conference contribution
VL - 2018-September
BT - 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
PB - IEEE Computer Society
ER -