{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:55:38Z","timestamp":1770814538024,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032163417","type":"print"},{"value":"9783032163424","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-16342-4_18","type":"book-chapter","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T08:58:13Z","timestamp":1770800293000},"page":"318-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Hardware Trojan Detection with\u00a0XGBoost Graph Learning: A Glass Box Approach"],"prefix":"10.1007","author":[{"given":"C.","family":"Sneha","sequence":"first","affiliation":[]},{"given":"M.","family":"Nirmala Devi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Hasegawa, K., Yanagisawa, M., Togawa, N.: Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. In: IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA, pp. 1\u20134. IEEE (2017). https:\/\/doi.org\/10.1109\/ISCAS.2017.8050827","DOI":"10.1109\/ISCAS.2017.8050827"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Helmy, M.M., Abdalla, M.A.Y., Khattab, A.: Detecting hardware trojans using structural features for hardware security and reliability. In: International Telecommunications Conference (ITC-Egypt), Cairo, Egypt, pp. 2345\u20132358. IEEE (2023). https:\/\/doi.org\/10.1109\/ITC-Egypt58155.2023.10206171","DOI":"10.1109\/ITC-Egypt58155.2023.10206171"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Negishi, R., Kurihara, T., Togawa, N.: Hardware-trojan detection at gate-level netlists using gradient boosting decision tree models. In: IEEE 12th International Conference on Consumer Electronics, Las Vegas, NV, USA (2022)","DOI":"10.1109\/ICCE-Berlin56473.2022.9937099"},{"key":"18_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/978-3-642-04138-9_28","volume-title":"Cryptographic Hardware and Embedded Systems - CHES 2009","author":"RS Chakraborty","year":"2009","unstructured":"Chakraborty, R.S., Wolff, F., Paul, S., Papachristou, C., Bhunia, S.: MERO: a statistical approach for hardware trojan detection. In: Clavier, C., Gaj, K. (eds.) CHES 2009. LNCS, vol. 5747, pp. 396\u2013410. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04138-9_28"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Hicks, M., Finnicum, M., King, S.T., Martin, M.M.K., Smith, J.M.: Overcoming an untrusted computing base: detecting and removing malicious hardware automatically. In: IEEE Symposium on Security and Privacy (SP), Oakland, CA, USA, pp. 159\u2013172. IEEE (2010). https:\/\/doi.org\/10.1109\/SP.2010.18","DOI":"10.1109\/SP.2010.18"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Waksman, A., Suozzo, M., Sethumadhavan, S.: FANCI: identification of stealthy malicious logic using boolean functional analysis. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), New York, NY, USA, pp. 697\u2013708. ACM (2013). https:\/\/doi.org\/10.1145\/2508859.2516654","DOI":"10.1145\/2508859.2516654"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Faezi, S., Yasaei, R., Al Faruque, M.A.: HTnet: transfer learning for golden chip-free hardware trojan detection. In: Design, Automation and Test in Europe Conference and Exhibition (DATE), Grenoble, France, pp. 1484\u20131489. IEEE (2021). https:\/\/doi.org\/10.23919\/DATE51398.2021.9474076","DOI":"10.23919\/DATE51398.2021.9474076"},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1109\/TIFS.2016.2613842","volume":"12","author":"H Salmani","year":"2017","unstructured":"Salmani, H.: COTD: reference-free hardware trojan detection and recovery based on controllability and observability in gate-level netlist. IEEE Trans. Inf. Forensics Secur. 12(2), 338\u2013350 (2017). https:\/\/doi.org\/10.1109\/TIFS.2016.2613842","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1007\/s10836-023-06080-9","volume":"39","author":"R Sharma","year":"2023","unstructured":"Sharma, R., Sharma, G.K., Pattanaik, M., Prashant, V.S.S.: Structural and SCOAP features based approach for hardware trojan detection using SHAP and light gradient boosting model. J. Electron. Test. 39, 465\u2013485 (2023)","journal-title":"J. Electron. Test."},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Hashemi, M., Momeni, A., Pashrashid, A., Mohammadi, S.: Graph centrality algorithms for hardware Trojan detection at gate-level netlists. Int. J. Eng. (IJE) Trans. A Basics 35(7), 1375\u20131387 (2022)","DOI":"10.5829\/IJE.2022.35.07A.16"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Pan, Z., Mishra, P.: Hardware Trojan detection using Shapley ensemble boosting. In: Proceedings of the 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023), pp. 496\u2013503 (2023)","DOI":"10.1145\/3566097.3567920"},{"issue":"3","key":"18_CR12","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/TC.2023.3280134","volume":"74","author":"K Hasegawa","year":"2025","unstructured":"Hasegawa, K., Yamashita, K., Hidano, S., Fukushima, K., Hashimoto, K., Togawa, N.: Node-wise hardware trojan detection based on graph learning. IEEE Trans. Comput. 74(3), 629\u2013642 (2025). https:\/\/doi.org\/10.1109\/TC.2023.3280134","journal-title":"IEEE Trans. Comput."},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Mahfuz, T., Gaikwad, P., Suha, T., Bhunia, S., Chakraborty, P.: SALTY: explainable artificial intelligence guided structural analysis for hardware Trojan detection. In: Proceedings of the 2025 IEEE 43rd VLSI Test Symposium (VTS), pp. 1\u20137. IEEE (2025)","DOI":"10.1109\/VTS65138.2025.11022818"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"99166","DOI":"10.1109\/ACCESS.2021.3094183","volume":"9","author":"A Alharbi","year":"2021","unstructured":"Alharbi, A., Alsubhi, K.: Botnet detection approach using graph-based machine learning. IEEE Access 9, 99166\u201399180 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3094183","journal-title":"IEEE Access"},{"key":"18_CR15","doi-asserted-by":"publisher","unstructured":"Goldstein, L.H., Thigpen, E.L.: SCOAP: sandia controllability\/observability analysis program. In: 17th Design Automation Conference (DAC), pp. 190\u2013196. IEEE (1980). https:\/\/doi.org\/10.1145\/800139.804528","DOI":"10.1145\/800139.804528"},{"key":"18_CR16","unstructured":"Batista, G.E.A.P.A., Bazzan, A.L.C., Monard, M.C.: Balancing training data for automated annotation of keywords: a case study. In: 2nd Brazilian Workshop on Bioinformatics (WOB 2003), Rio de Janeiro, Brazil, pp. 10\u201318. IEEE (2003)"},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 30, pp. 4768\u20134777. Long Beach, CA, USA (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.07874","DOI":"10.48550\/arXiv.1705.07874"},{"key":"18_CR18","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1135\u20131144. ACM, San Francisco, CA, USA (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Makridis, G., et al.: Towards a unified multidimensional explainability metric: evaluating trustworthiness in AI models. In: Proceedings of the 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT). IEEE (2023)","DOI":"10.1109\/DCOSS-IoT58021.2023.00084"},{"key":"18_CR20","unstructured":"Trust-HUB. http:\/\/www.trust-hub.org. Accessed 30 Aug 2025"}],"container-title":["Lecture Notes in Computer Science","Security, Privacy, and Applied Cryptography Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16342-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T08:58:18Z","timestamp":1770800298000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16342-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032163417","9783032163424"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16342-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"SPACE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security, Privacy, and Applied Cryptography Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guwahati","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"space2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.iitg.ac.in\/space2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}