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Syst."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>\n            With the growth and globalization of IC design and development, there is an increase in the number of Designers and Design houses. As setting up a fabrication facility may easily cost upwards of $20 billion, costs for advanced nodes may be even greater. IC design houses that cannot produce their chips in-house have no option but to use external foundries that are often in other countries. Establishing trust with these external foundries can be a challenge, and these foundries are assumed to be untrusted. The use of these untrusted foundries in the global semiconductor supply chain has raised concerns about the security of the fabricated ICs targeted for sensitive applications. One of these security threats is the adversarial infestation of fabricated ICs with a\n            <jats:bold>Hardware Trojan (HT)<\/jats:bold>\n            . An HT can be broadly described as a malicious modification to a circuit to control, modify, disable, or monitor its logic. Conventional VLSI manufacturing tests and verification methods fail to detect HT due to the different and un-modeled nature of these malicious modifications. Current state-of-the-art HT detection methods utilize statistical analysis of various side-channel information collected from ICs, such as power analysis, power supply transient analysis, regional supply current analysis, temperature analysis, wireless transmission power analysis, and delay analysis. To detect HTs, most methods require a Trojan-free reference golden IC. A signature from these golden ICs is extracted and used to detect ICs with HTs. However, access to a golden IC is not always feasible. Thus, a mechanism for HT detection is sought that does not require the golden IC.\n            <jats:bold>Machine Learning (ML)<\/jats:bold>\n            approaches have emerged to be extremely useful in helping eliminate the need for a golden IC. Recent works on utilizing ML for HT detection have been shown to be promising in achieving this goal. Thus, in this tutorial, we will explain utilizing ML as a solution to the challenge of HT detection. Additionally, we will describe the\n            <jats:bold>Electronic Design Automation (EDA)<\/jats:bold>\n            tool flow for automating ML-assisted HT detection. Moreover, to further discuss the benefits of ML-assisted HT detection solutions, we will demonstrate a\n            <jats:bold>Neural Network (NN)<\/jats:bold>\n            -assisted timing profiling method for HT detection. Finally, we will discuss the shortcomings and open challenges of ML-assisted HT detection methods.\n          <\/jats:p>","DOI":"10.1145\/3579823","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T11:26:12Z","timestamp":1674041172000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":66,"title":["Hardware Trojan Detection Using Machine Learning: A Tutorial"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-0457","authenticated-orcid":false,"given":"Kevin Immanuel","family":"Gubbi","sequence":"first","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3735-9191","authenticated-orcid":false,"given":"Banafsheh","family":"Saber Latibari","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4026-5516","authenticated-orcid":false,"given":"Anirudh","family":"Srikanth","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0163-5449","authenticated-orcid":false,"given":"Tyler","family":"Sheaves","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-1606","authenticated-orcid":false,"given":"Sayed Arash","family":"Beheshti-Shirazi","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-2387","authenticated-orcid":false,"given":"Sai Manoj","family":"PD","sequence":"additional","affiliation":[{"name":"George Mason University, Fairfax, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2035-8512","authenticated-orcid":false,"given":"Satareh","family":"Rafatirad","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4052-8075","authenticated-orcid":false,"given":"Avesta","family":"Sasan","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4052-1663","authenticated-orcid":false,"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"University of California, Davis, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5998-8795","authenticated-orcid":false,"given":"Soheil","family":"Salehi","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, Arizona, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1015047.1015049"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2012.200"},{"key":"e_1_3_1_4_2","first-page":"3","volume-title":"2008 IEEE International Workshop on Hardware-Oriented Security and Trust","author":"Rad Reza","year":"2008","unstructured":"Reza Rad, Jim Plusquellic, and Mohammad Tehranipoor. 2008. Sensitivity analysis to hardware Trojans using power supply transient signals. In 2008 IEEE International Workshop on Hardware-Oriented Security and Trust. IEEE, 3\u20137."},{"key":"e_1_3_1_5_2","first-page":"173","volume-title":"International Workshop on Cryptographic Hardware and Embedded Systems","author":"Du Dongdong","year":"2010","unstructured":"Dongdong Du, Seetharam Narasimhan, Rajat Subhra Chakraborty, and Swarup Bhunia. 2010. Self-referencing: A scalable side-channel approach for hardware Trojan detection. In International Workshop on Cryptographic Hardware and Embedded Systems. 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IEEE, 151\u2013156."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/2966986.2967061"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2593069.2593147"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3093160"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2013.6657085"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41635-017-0001-6"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/MDT.2007.79"},{"issue":"7","key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1587\/transfun.E100.A.1427","article-title":"A hardware-Trojan classification method using machine learning at gate-level netlists based on Trojan features","volume":"100","author":"Hasegawa Kento","year":"2017","unstructured":"Kento Hasegawa, Masao Yanagisawa, and Nozomu Togawa. 2017. 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ATTRACT\u201920."},{"key":"e_1_3_1_29_2","article-title":"Hardware Trojan detection using graph neural networks","author":"Yasaei Rozhin","year":"2022","unstructured":"Rozhin Yasaei, Luke Chen, Shih-Yuan Yu, and Mohammad Abdullah Al Faruque. 2022. Hardware Trojan detection using graph neural networks. arXiv preprint arXiv:2204.11431 (2022).","journal-title":"arXiv preprint arXiv:2204.11431"},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.23919\/DATE51398.2021.9474174","volume-title":"2021 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201921)","author":"Yasaei Rozhin","year":"2021","unstructured":"Rozhin Yasaei, Shih-Yuan Yu, and Mohammad Abdullah Al Faruque. 2021. GNN4TJ: Graph neural networks for hardware Trojan detection at register transfer level. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201921). 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AdaTest: Reinforcement learning and adaptive sampling for on-chip hardware Trojan detection. arXiv preprint arXiv:2204.06117 (2022).","journal-title":"arXiv preprint arXiv:2204.06117"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2206781.2206806"},{"key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1145\/3287624.3287683","volume-title":"Proceedings of the 24th Asia and South Pacific Design Automation Conference","author":"Vakil Ashkan","year":"2019","unstructured":"Ashkan Vakil, Houman Homayoun, and Avesta Sasan. 2019. IR-ATA: IR annotated timing analysis, a flow for closing the loop between PDN design, IR analysis & timing closure. In Proceedings of the 24th Asia and South Pacific Design Automation Conference. 152\u2013159."},{"key":"e_1_3_1_35_2","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/SP.2007.36","volume-title":"2007 IEEE Symposium on Security and Privacy (SP\u201907)","author":"Agrawal Dakshi","year":"2007","unstructured":"Dakshi Agrawal, Selcuk Baktir, Deniz Karakoyunlu, Pankaj Rohatgi, and Berk Sunar. 2007. Trojan detection using IC fingerprinting. In 2007 IEEE Symposium on Security and Privacy (SP\u201907). IEEE, 296\u2013310."},{"issue":"12","key":"e_1_3_1_36_2","first-page":"1735","article-title":"A sensitivity analysis of power signal methods for detecting hardware Trojans under real process and environmental conditions","volume":"18","author":"Rad Reza","year":"2009","unstructured":"Reza Rad, Jim Plusquellic, and Mohammad Tehranipoor. 2009. A sensitivity analysis of power signal methods for detecting hardware Trojans under real process and environmental conditions. 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IEEE, 66\u201373."},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2011.2147341"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"issue":"4","key":"e_1_3_1_40_2","first-page":"1","article-title":"XGBoost: Extreme gradient boosting","volume":"1","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, et\u00a0al. 2015. XGBoost: Extreme gradient boosting. R Package Version 0.4-2 1, 4 (2015), 1\u20134.","journal-title":"R Package Version 0.4-2"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2651396"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00503.x"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1214\/08-AOS625"},{"issue":"10","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1002\/bjs.10895","article-title":"LASSO regression","volume":"105","author":"Ranstam J.","year":"2018","unstructured":"J. Ranstam and J. A. Cook. 2018. LASSO regression. Journal of British Surgery 105, 10 (2018), 1348\u20131348.","journal-title":"Journal of British Surgery"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00627.x"},{"issue":"1","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/wics.14","article-title":"Ridge regression","volume":"1","author":"McDonald Gary C.","year":"2009","unstructured":"Gary C. McDonald. 2009. Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics 1, 1 (2009), 93\u2013100.","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"e_1_3_1_48_2","article-title":"Multilayer perceptron tutorial","author":"Noriega Leonardo","year":"2005","unstructured":"Leonardo Noriega. 2005. Multilayer perceptron tutorial. School of Computing. Staffordshire University (2005).","journal-title":"School of Computing. Staffordshire University"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2016.01.011"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-016-0481-7"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2014.2354293"},{"key":"e_1_3_1_52_2","first-page":"1","volume-title":"2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN\u201916)","author":"Shende Roshni","year":"2016","unstructured":"Roshni Shende and Dayanand D. Ambawade. 2016. A side channel based power analysis technique for hardware Trojan detection using statistical learning approach. In 2016 Thirteenth International Conference on Wireless and Optical Communications Networks (WOCN\u201916). IEEE, 1\u20134."},{"key":"e_1_3_1_53_2","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).","journal-title":"arXiv preprint arXiv:1412.6980"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00638"},{"key":"e_1_3_1_55_2","volume-title":"International Workshop for Logic Synthesis (IWLS):","author":"Albrecht Christoph","year":"2005","unstructured":"Christoph Albrecht. 2005. IWLS 2005 benchmarks. In International Workshop for Logic Synthesis (IWLS):http:\/\/www.iwls.org."},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwj063"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3526241.3530379"},{"key":"e_1_3_1_58_2","article-title":"Hardware Trojan detection using unsupervised deep learning on quantum diamond microscope magnetic field images","author":"Ashok Maitreyi","year":"2022","unstructured":"Maitreyi Ashok, Matthew J. Turner, Ronald L. Walsworth, Edlyn V. Levine, and Anantha P. Chandrakasan. 2022. Hardware Trojan detection using unsupervised deep learning on quantum diamond microscope magnetic field images. ACM Journal on Emerging Technologies in Computing Systems (JETC) (2022).","journal-title":"ACM Journal on Emerging Technologies in Computing Systems (JETC)"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.2022.9771357"},{"key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"508","DOI":"10.23919\/DATE48585.2020.9116483","volume-title":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201920)","author":"Knechtel Johann","year":"2020","unstructured":"Johann Knechtel, Elif Bilge Kavun, Francesco Regazzoni, Annelie Heuser, Anupam Chattopadhyay, Debdeep Mukhopadhyay, Soumyajit Dey, Yunsi Fei, Yaacov Belenky, Itamar Levi, et\u00a0al. 2020. Towards secure composition of integrated circuits and electronic systems: On the role of EDA. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE\u201920). IEEE, 508\u2013513."},{"key":"e_1_3_1_61_2","article-title":"Adversarial attacks on neural network policies","author":"Huang Sandy","year":"2017","unstructured":"Sandy Huang, Nicolas Papernot, Ian Goodfellow, Yan Duan, and Pieter Abbeel. 2017. Adversarial attacks on neural network policies. arXiv preprint arXiv:1702.02284 (2017).","journal-title":"arXiv preprint arXiv:1702.02284"},{"key":"e_1_3_1_62_2","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry Aleksander","year":"2017","unstructured":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017).","journal-title":"arXiv preprint arXiv:1706.06083"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3526241.3530831"},{"key":"e_1_3_1_66_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/ISQED48828.2020.9137007","volume-title":"2020 21st International Symposium on Quality Electronic Design (ISQED\u201920)","author":"Vakil Ashkan","year":"2020","unstructured":"Ashkan Vakil, Farnaz Behnia, Ali Mirzaeian, Houman Homayoun, Naghmeh Karimi, and Avesta Sasan. 2020. LASCA: Learning assisted side channel delay analysis for hardware Trojan detection. In 2020 21st International Symposium on Quality Electronic Design (ISQED\u201920). 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