Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 2320-2326 |
| Number of pages | 7 |
| Journal | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| Volume | E101A |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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Keywords
- Detection
- Gate-level netlist
- Hardware Trojan
- Machine learning
- Multi-layer neural networks
ASJC Scopus subject areas
- Signal Processing
- Computer Graphics and Computer-Aided Design
- Electrical and Electronic Engineering
- Applied Mathematics
Cite this
Empirical evaluation and optimization of hardware-Trojan classification for gate-level netlists based on multi-layer neural networks. / Hasegawa, Kento; Yanagisawa, Masao; Togawa, Nozomu.
In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E101A, No. 12, 01.12.2018, p. 2320-2326.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Empirical evaluation and optimization of hardware-Trojan classification for gate-level netlists based on multi-layer neural networks
AU - Hasegawa, Kento
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Detection
KW - Gate-level netlist
KW - Hardware Trojan
KW - Machine learning
KW - Multi-layer neural networks
UR - http://www.scopus.com/inward/record.url?scp=85057466201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057466201&partnerID=8YFLogxK
U2 - 10.1587/transfun.E101.A.2320
DO - 10.1587/transfun.E101.A.2320
M3 - Article
AN - SCOPUS:85057466201
VL - E101A
SP - 2320
EP - 2326
JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
SN - 0916-8508
IS - 12
ER -