{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:32:20Z","timestamp":1785421940042,"version":"3.56.0"},"reference-count":17,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,2,1]]},"DOI":"10.1587\/transfun.2024eap1041","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T22:14:04Z","timestamp":1723587244000},"page":"77-82","source":"Crossref","is-referenced-by-count":1,"title":["Hardware Trojan Detection Method Based on Enhanced Local Outlier Factor"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Tingyuan","family":"NIE","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"NIE","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"ZHAO","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] A.J. Tiempo, and Y.J. Jeong, \u201cSplit and eliminate: A region-based segmentation for hardware Trojan detection,\u201d IEICE Trans. Inf. & Syst., vol.E106-D, no.3, pp.349-356, March 2023. 10.1587\/transinf.2022edp7169","DOI":"10.1587\/transinf.2022EDP7169"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] C. Dong, Y. Liu, J. Chen, X. Liu, W. Guo, and Y. Chen, \u201cAn unsupervised detection approach for hardware trojans,\u201d IEEE Access, vol.8, pp.158169-158183, 2020. 10.1109\/access.2020.3001239","DOI":"10.1109\/ACCESS.2020.3001239"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] K. Hasegawa, M. Oya, M. Yanagisawa, and N. Togawa, \u201cHardware Trojans classification for gate-level netlists based on machine learning,\u201d IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS), pp.203-206, 2016. 10.1109\/iolts.2016.7604700","DOI":"10.1109\/IOLTS.2016.7604700"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] C.H. Kok, C.Y. Ooi, M. Moghbel, N. Ismail, H.S. Choo, and M. Inoue, \u201cClassification of Trojan nets based on SCOAP values using supervised learning,\u201d Proc. IEEE international Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, pp.1-5, 2019. 10.1109\/iscas.2019.8702462","DOI":"10.1109\/ISCAS.2019.8702462"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] K. Hasegawa, M. Yanagisawa, and N. Togawa, \u201cA hardware-Trojan classification method using machine learning at gate-level netlists based on Trojan features,\u201d IEICE Trans. Fundamentals, vol.E100-A, no.7, pp.1427-1438, July 2017. 10.1587\/transfun.e100.a.1427","DOI":"10.1587\/transfun.E100.A.1427"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] K. Hasegawa, M. Yanagisawa, and N. Togawa, \u201cTrojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier,\u201d Proc. IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA, pp.1-4, 2017. 10.1109\/iscas.2017.8050827","DOI":"10.1109\/ISCAS.2017.8050827"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] K. Hasegawa, M. Yanagisawa, and N. Togawa, \u201cHardware Trojans classification for gate-level netlists using multi-layer neural networks,\u201d IEEE International Symposium on On-Line Testing & Robust System Design, 2017. 10.1109\/iolts.2017.8046227","DOI":"10.1109\/IOLTS.2017.8046227"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] C. Dong, J. Chen, W. Guo, and J. Zou, \u201cA machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things,\u201d International Journal of Distributed Sensor Networks, vol.15, no.12, p.1550147719888098, 2019. 10.1177\/1550147719888098","DOI":"10.1177\/1550147719888098"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] K. Hasegawa, M. Yanagisawa, and N. Togawa, \u201cA hardware-Trojan classification method utilizing boundary net structures,\u201d IEEE International Conference on Consumer Electronics (ICCE), pp.1-4, 2018. 10.1109\/icce.2018.8326247","DOI":"10.1109\/ICCE.2018.8326247"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] M. Priyatharishini, M.N. Devi, \u201cA deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability,\u201d Measurement, vol.194, p.111055, 2022. 10.1016\/j.measurement.2022.111055","DOI":"10.1016\/j.measurement.2022.111055"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] H. Salmani, \u201cCOTD: Reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist,\u201d IEEE Trans. Inf. Forensics Security, vol.12, no.2, pp.338-350, 2016. 10.1109\/tifs.2016.2613842","DOI":"10.1109\/TIFS.2016.2613842"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] O. Alghushairy, R. Alsini, T. Soule, and X. Ma, \u201cA review of local outlier factor algorithms for outlier detection in big data streams,\u201d Big Data Cogn. Comput., vol.5, no.1, pp.1-2, 2020. 10.3390\/bdcc5010001","DOI":"10.3390\/bdcc5010001"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] S. Meenakshi and Nirmala Devi M,, \u201cPerformance enhancement of unsupervised hardware Trojan detection algorithm using clustering-based local outlier factor technique for design security,\u201d IEEE International Test Conference India (ITC India), pp.1-8, 2022. 10.1109\/itcindia202255192.2022.9854569","DOI":"10.1109\/ITCIndia202255192.2022.9854569"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Yao, X. Chen, J. Zhang, Q. Liu, and H. Yang, \u201cFASTrust: Feature analysis for third-party IP trust verification,\u201d IEEE International Test Conference, 2015. 10.1109\/test.2015.7342417","DOI":"10.1109\/TEST.2015.7342417"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C.H. Kok, C.Y. Ooi, M. Inoue, M. Moghbel, and F. Hussin, \u201cNet classification based on testability and netlist structural features for hardware Trojan detection,\u201d IEEE 28th Asian Test Symposium (ATS), 2019. 10.1109\/ats47505.2019.00020","DOI":"10.1109\/ATS47505.2019.00020"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] L.H. Goldstein and E.L. Thigpen, \u201cSCOAP: Sandia controllability\/observability analysis program,\u201d Proc. Design Automation Conference, pp.190-196, 1980. 10.1145\/800139.804528","DOI":"10.1145\/800139.804528"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] K. Huang and Y. He, \u201cTrigger identification using difference-amplified controllability and dynamic transition probability for hardware Trojan detection,\u201d IEEE Trans. Inf. Forensics Security, vol.15, pp.3387-3400, 2020. 10.1109\/tifs.2019.2946044","DOI":"10.1109\/TIFS.2019.2946044"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/2\/E108.A_2024EAP1041\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T03:29:41Z","timestamp":1738380581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/2\/E108.A_2024EAP1041\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":17,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2024eap1041","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"value":"0916-8508","type":"print"},{"value":"1745-1337","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,1]]},"article-number":"2024EAP1041"}}