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Hardware Trojan detection research based on MLP
| Content Provider | Scilit |
|---|---|
| Author | Ding, Qiao Yin, Shizhuang Liu, Lijun Wang, Chao |
| Copyright Year | 2020 |
| Description | Journal: Journal of Physics: Conference Series In view of the variety of Hardware Trojan (HT) and the difficulty of obtaining unknown Trojan characteristics in side-channel signals by conventional methods. In this paper, MLP was selected to establish the network model by means of supervised learning, the method took supervised learning ways to build neural network model for feature extraction and discrimination of side channel information. A verification system was set up based on FPGA to obtain side-channel information. The results show that the detection rate of the MLP is more than 1% higher than that of the traditional support vector machine (SVM) method when detecting the hardware Trojan horse with the parent circuit area of 2%. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1684/1/012065/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1684/1/012065 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1684 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-10-31 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Hardware and Architecture Hardware Trojan |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
