@inproceedings{scholars11018,
            note = {cited By 6; Conference of 28th IEEE Asian Test Symposium, ATS 2019 ; Conference Date: 10 December 2019 Through 13 December 2019; Conference Code:156685},
             doi = {10.1109/ATS47505.2019.00018},
           title = {Machine-Learning-Based Multiple Abstraction-Level Detection of Hardware Trojan Inserted at Register-Transfer Level},
         journal = {Proceedings of the Asian Test Symposium},
          volume = {2019-D},
       publisher = {IEEE Computer Society},
           pages = {98},
            year = {2019},
            issn = {10817735},
        keywords = {Integrated circuits; Learning systems; Machine learning; Malware, Abstraction level; False positive detection; Gate levels; Hardware Trojan detection; Machine learning approaches; Register transfer level; Trojan detections; Trojans, Hardware security},
        abstract = {Hardware Trojan refers to a malicious modification of an integrated circuit (IC). To eliminate the complications arising from designing an IC which includes a Trojan, it is suggested to apply Trojan detection as early as at register-transfer level (RTL). In this paper, we propose a hardware Trojan detection framework which consists of both RTL and gate-level classification using machine learning approaches to detect hardware Trojan inserted at RTL. In the experiment, all Trojan benchmarks were successfully identified without false positive detection on non-Trojan benchmark. {\^A}{\copyright} 2019 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078343218&doi=10.1109\%2fATS47505.2019.00018&partnerID=40&md5=c5da625563674630028625dd473f86be},
          author = {Choo, H. S. and Ooi, C. Y. and Inoue, M. and Ismail, N. and Moghbel, M. and Baskara Dass, S. and Kok, C. H. and Hussin, F. A.},
            isbn = {9781728126951}
}