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The efficient detection of these malicious Trojan circuits is of utmost significance, as it holds paramount importance in cultivating trust within the semiconductor IC supply chain. However, prevailing detection methodologies, predominantly reliant on Side-Channel Analysis (SCA), often necessitate the utilization of golden chips for validation. This article heralds a new era in hardware Trojan detection, harnessing the prowess of unsupervised machine learning in conjunction with SCA to eliminate the need for golden data. Employing unsupervised clustering, the methodology not only showcased a superior false-positive rate but also demonstrated a comparable accuracy level when compared to supervised counterparts. Notably, the proposed model exhibited an impressive accuracy rate of 93%, particularly excelling in pinpointing diminutive Trojans triggered by concise events, surpassing the capabilities of preceding techniques. In conclusion, this research advances a paradigm in hardware Trojan detection, emphasizing its potential in enhancing the integrity of semiconductor IC supply chains.<\/jats:p>","DOI":"10.1145\/3748652","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T19:53:08Z","timestamp":1752522788000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Golden-Free Unsupervised ML-Assisted Security Approach for Detection of IC Hardware Trojans"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6210-1219","authenticated-orcid":false,"given":"Ashutosh","family":"Ghimire","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Wright State University, Dayton, Ohio, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1337-419X","authenticated-orcid":false,"given":"Mohammed","family":"Alkurdi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Wright State University, Dayton, Ohio, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0010-6388","authenticated-orcid":false,"given":"Md Tauhidur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Florida International University, Miami, Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2959-6541","authenticated-orcid":false,"given":"Saraju","family":"Mohanty","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of North Texas, Denton, Texas, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7126-5984","authenticated-orcid":false,"given":"Fathi","family":"Amsaad","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Wright State University, Dayton, Ohio, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"issue":"42","key":"e_1_3_1_2_2","first-page":"1","article-title":"An efficient and effective generic agglomerative hierarchical clustering approach","volume":"19","author":"Ah-Pine Julien","year":"2018","unstructured":"Julien Ah-Pine. 2018. 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