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These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under standard test vectors, and the changes they bring into power consumption, timing, and area are minimal. We present a dual-domain feature extraction strategy that combines time-domain features with frequency-domain characteristics of power traces. For the detection, we created an artificial intelligence (AI)\u00a0based robust Trojan detection framework that integrates traditional machine learning models, such as random forest\u00a0(RF), gradient boosting\u00a0(GB), na\u00efve bayes\u00a0(NB), and deep learning models, such as deep neural network\u00a0(DNN), long short-term memory (LSTM), and graph neural network (GNN). In this study, we consider these models as baseline AI models to detect Trojan-infected circuits via side-channel power analysis. We employed a stacked ensemble classifier that integrates the distinct strengths of the six baseline models used in this study. After evaluating our stacking ensemble-based detection method on the advanced encryption standard (AES)-Trojan benchmark, which\u00a0covers diverse Trojan types, the results demonstrate that the\u00a0ensemble method consistently\u00a0outperformed all six baseline models.\u00a0The ensemble-based detection method achieved\u00a0a macro-averaged area under the receiver operating characteristic (ROC) curve (AUC) of 0.987, while remaining golden-chip-free, meaning it does not rely on a trusted reference IC for baseline comparison. Instead it\u00a0detects anomalies directly from observable characteristics\u00a0of untrusted chips, such as side-channel emissions.<\/jats:p>","DOI":"10.1186\/s42400-025-00542-7","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T06:49:48Z","timestamp":1769150988000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust hardware Trojan detection leveraging dual-domain features and stacked ensemble learning"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2755-1994","authenticated-orcid":false,"given":"Sefatun-Noor","family":"Puspa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1599-5472","authenticated-orcid":false,"given":"Abyad","family":"Enan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4659-7506","authenticated-orcid":false,"given":"Reek","family":"Majumder","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7326-3694","authenticated-orcid":false,"given":"M Sabbir","family":"Salek","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2373-5013","authenticated-orcid":false,"given":"Gurcan","family":"Comert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3275-6983","authenticated-orcid":false,"given":"Mashrur","family":"Chowdhury","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"issue":"12","key":"542_CR1","doi-asserted-by":"publisher","first-page":"8082","DOI":"10.1109\/TAP.2020.3000562","volume":"68","author":"S Adibelli","year":"2020","unstructured":"Adibelli S, Juyal P, Nguyen LN, Prvulovic M, Zajic A (2020) Near-field backscattering-based sensing for hardware Trojan detection. 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