{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T18:06:04Z","timestamp":1772733964537,"version":"3.50.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Electron Test"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10836-023-06079-2","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T07:01:46Z","timestamp":1694070106000},"page":"447-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Threshold Analysis Using Probabilistic Xgboost Classifier for Hardware Trojan Detection"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4848-5524","authenticated-orcid":false,"given":"Tapobrata","family":"Dhar","sequence":"first","affiliation":[]},{"given":"Ranit","family":"Das","sequence":"additional","affiliation":[]},{"given":"Chandan","family":"Giri","sequence":"additional","affiliation":[]},{"given":"Surajit Kumar","family":"Roy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"6079_CR1","doi-asserted-by":"publisher","unstructured":"Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Proc. Noise Reduction in Speech Processing. Springer Topics in Signal Processing, pp. 1\u20134. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-00296-0_5","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"6079_CR2","doi-asserted-by":"publisher","unstructured":"Brglez F, Bryan D, Kozminski K (1989) Combinational profiles of sequential benchmark circuits. In: Proc. 1989 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1929\u201319343. https:\/\/doi.org\/10.1109\/ISCAS.1989.100747","DOI":"10.1109\/ISCAS.1989.100747"},{"key":"6079_CR3","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proc. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201916, pp. 785\u2013794. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"6079_CR4","doi-asserted-by":"publisher","unstructured":"Das R, Dhar T, Roy SK (2022) A threshold based hardware trojan detection technique using xgboost algorithm. In: Proc. 2022 IEEE International Test Conference India (ITC India), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ITCIndia202255192.2022.9854735","DOI":"10.1109\/ITCIndia202255192.2022.9854735"},{"issue":"3","key":"6079_CR5","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1145\/3439951","volume":"17","author":"T Dhar","year":"2021","unstructured":"Dhar T, Roy SK, Giri C (2021) Hardware Trojan Horse Detection through Improved Switching of Dormant Nets. ACM J Emerg Technol Comput Syst 17(3):33\u201313322. https:\/\/doi.org\/10.1145\/3439951","journal-title":"ACM J Emerg Technol Comput Syst"},{"key":"6079_CR6","doi-asserted-by":"crossref","unstructured":"Dong C, Chen J, Guo W, Zou J (2019) A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things.\u00a0Int J Distrib Sens Netw\u00a015(12):1550147719888098","DOI":"10.1177\/1550147719888098"},{"key":"6079_CR7","doi-asserted-by":"publisher","first-page":"158169","DOI":"10.1109\/ACCESS.2020.3001239","volume":"8","author":"C Dong","year":"2020","unstructured":"Dong C, Liu Y, Chen J, Liu X, Guo W, Chen Y (2020) An unsupervised detection approach for hardware trojans. IEEE Access: Practical Innovations, Open Solutions 8:158169\u2013158183. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001239","journal-title":"IEEE Access: Practical Innovations, Open Solutions"},{"key":"6079_CR8","doi-asserted-by":"publisher","unstructured":"Goldstein LH, Thigpen EL (1980) SCOAP: Sandia Controllability\/Observability analysis program. In: Proc. 17th Design Automation Conference, pp. 190\u2013196. https:\/\/doi.org\/10.1109\/DAC.1980.1585245","DOI":"10.1109\/DAC.1980.1585245"},{"key":"6079_CR9","doi-asserted-by":"publisher","unstructured":"Hasegawa K, Oya M, Yanagisawa M, Togawa N (2016) Hardware trojans classification for gate-level netlists based on machine learning. In: Proc. 2016 IEEE 22nd International Symposium on On-Line Testing and Robust System Design (IOLTS), pp. 203\u2013206. https:\/\/doi.org\/10.1109\/IOLTS.2016.7604700","DOI":"10.1109\/IOLTS.2016.7604700"},{"key":"6079_CR10","doi-asserted-by":"publisher","unstructured":"Hasegawa K, Yanagisawa M, Togawa N (2017) Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. In: Proc. 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20134. https:\/\/doi.org\/10.1109\/ISCAS.2017.8050827","DOI":"10.1109\/ISCAS.2017.8050827"},{"key":"6079_CR11","doi-asserted-by":"publisher","unstructured":"Hasegawa K, Yanagisawa M, Togawa N (2017) Hardware Trojans classification for gate-level netlists using multi-layer neural networks. In: Proc. 2017 IEEE 23rd International Symposium on On-Line Testing and Robust System Design (IOLTS), pp. 227\u2013232. https:\/\/doi.org\/10.1109\/IOLTS.2017.8046227","DOI":"10.1109\/IOLTS.2017.8046227"},{"key":"6079_CR12","doi-asserted-by":"publisher","unstructured":"Hoque T, Cruz J, Chakraborty P, Bhunia S (2018) Hardware IP Trust Validation: Learn (the Untrustworthy), and Verify. In: Proc. 2018 IEEE International Test Conference (ITC), pp. 1\u201310. https:\/\/doi.org\/10.1109\/TEST.2018.8624727","DOI":"10.1109\/TEST.2018.8624727"},{"key":"6079_CR13","doi-asserted-by":"publisher","first-page":"10796","DOI":"10.1109\/ACCESS.2020.2965016","volume":"8","author":"Z Huang","year":"2020","unstructured":"Huang Z, Wang Q, Chen Y, Jiang X (2020) A survey on machine learning against Hardware trojan attacks: Recent advances and challenges. IEEE Access 8:10796\u201310826. https:\/\/doi.org\/10.1109\/ACCESS.2020.2965016","journal-title":"IEEE Access"},{"issue":"6","key":"6079_CR14","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1049\/iet-cdt.2014.0039","volume":"8","author":"N Jacob","year":"2014","unstructured":"Jacob N, Merli D, Heyszl J, Sigl G (2014) Hardware Trojans Current challenges and approaches. IET Comput Digit Tech 8(6):264\u2013273. https:\/\/doi.org\/10.1049\/iet-cdt.2014.0039","journal-title":"IET Comput Digit Tech"},{"key":"6079_CR15","doi-asserted-by":"publisher","unstructured":"Kok CH, Ooi CY, Moghbel M, Ismail N, Choo HS, Inoue M (2019) Classification of trojan nets based on SCOAP values using supervised learning. In: Proc. 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2019.8702462","DOI":"10.1109\/ISCAS.2019.8702462"},{"key":"6079_CR16","doi-asserted-by":"publisher","unstructured":"Li H, Liu Q, Zhang J, Lyu Y (2015) A survey of hardware trojan detection, diagnosis and prevention. In: Proc. 2015 14th International Conference on Computer-Aided Design and Computer Graphics (CAD\/Graphics), pp. 173\u2013180. https:\/\/doi.org\/10.1109\/CADGRAPHICS.2015.41","DOI":"10.1109\/CADGRAPHICS.2015.41"},{"issue":"8","key":"6079_CR17","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1109\/JPROC.2014.2335155","volume":"102","author":"M Rostami","year":"2014","unstructured":"Rostami M, Koushanfar F, Karri R (2014) A primer on hardware security: Models, methods, and metrics. Proc IEEE 102(8):1283\u20131295. https:\/\/doi.org\/10.1109\/JPROC.2014.2335155","journal-title":"Proc IEEE"},{"issue":"2","key":"6079_CR18","first-page":"169","volume":"41","author":"RM Sakia","year":"1992","unstructured":"Sakia RM (1992) The box-cox transformation technique: A review. J R Stat Soc Ser A Stat Soc Series D 41(2):169\u2013178","journal-title":"J R Stat Soc Ser A Stat Soc Series D"},{"key":"6079_CR19","unstructured":"Salmani H, Tehranipoor M (2023) Trust-Hub.Org. https:\/\/trust-hub.org\/#\/home"},{"key":"6079_CR20","doi-asserted-by":"publisher","unstructured":"Sharma R, Valivati NK, Sharma GK, Pattanaik M (2020) A new hardware trojan detection technique using class weighted XGBoost classifier. In: Proc. 2020 24th International Symposium on VLSI Design and Test (VDAT), pp. 1\u20136. https:\/\/doi.org\/10.1109\/VDAT50263.2020.9190603","DOI":"10.1109\/VDAT50263.2020.9190603"},{"issue":"4","key":"6079_CR21","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/s10836-021-05960-2","volume":"37","author":"M Tebyanian","year":"2021","unstructured":"Tebyanian M, Mokhtarpour A, Shafieinejad A (2021) SC-COTD: Hardware trojan detection based on sequential\/combinational testability features using ensemble classifier. J Electron Test 37(4):473\u2013487. https:\/\/doi.org\/10.1007\/s10836-021-05960-2","journal-title":"J Electron Test"},{"key":"6079_CR22","doi-asserted-by":"publisher","unstructured":"Yang Y, Ye J, Cao Y, Zhang J, Li X, Li H, Hu Y (2020) Survey: Hardware trojan detection for netlist. In: Proc. 2020 IEEE 29th Asian Test Symposium (ATS), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ATS49688.2020.9301614","DOI":"10.1109\/ATS49688.2020.9301614"}],"container-title":["Journal of Electronic Testing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10836-023-06079-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10836-023-06079-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10836-023-06079-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T07:12:32Z","timestamp":1696057952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10836-023-06079-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["6079"],"URL":"https:\/\/doi.org\/10.1007\/s10836-023-06079-2","relation":{},"ISSN":["0923-8174","1573-0727"],"issn-type":[{"value":"0923-8174","type":"print"},{"value":"1573-0727","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"17 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interests"}}]}}