[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128120-en":3,"doc-seo-128120-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128120,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Detecting Compromised Hardware Integrity with Machine Learning in Multicore Processors","Due to globalization in integrated circuit manufacturing, trust in hardware reliability has been undermined by counterfeit devices and related threats such as hardware trojans, hardware backdoors, IP theft, and counterfeit components. The thesis targets counterfeit detection challenges caused by increasing hardware complexity and the limitations of physical inspection and costly, extensive electrical testing. It proposes comparing internal data traffic to a trusted golden device using machine learning, specifically One Class Support Vector Machine (OC-SVM).","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n4-2025  \nDetecting Compromised Hardware Integrity with Machine Learning in Multicore Processors  \nAlexander Beekman [ajb3715@rit.edu](ajb3715@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nBeekman, Alexander, \"Detecting Compromised Hardware Integrity with Machine Learning in Multicore Processors\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nDetecting Compromised Hardware Integrity with Machine Learning in Multicore Processors  \nAlexander Beekman  \nDetecting Compromised Hardware Integrity with Machine Learning in Multicore Processors  \nAlexander Beekman  \nApril 2025  \nDr. Amlan Ganguly-Advisor  \nDr. Michael Zuzak-Committee member  \nProfessor Mark Indovina-Committee member  \nA Thesis Submitted  \nin Partial Fulfillment  \nof the Requirements for the Degree of  \nMaster of Science  \nin  \nComputer Engineering  \nDepartment of Computer Engineering  \nAbstract  \nDue to the globalization of the Integrated Circuits (IC) manufacturing process, the trust surrounding hardware reliability has become compromised through methods such as injection of malicious hardware (including hardware trojans), hardware backdoors, IP theft, and counterfeit hardware. This thesis will focus on the issue of counterfeit hardware. As the complexity of modern day hardware has increased, detecting counterfeits has become increasingly difficult. Current methods of physical and electrical inspection are limited in both depth and size limitations. Physical inspections are only useful for identifying counterfeits that manifest physically, while electrical testing requires expensive and extensive setups. Computational evaluation of all possible states of the device can at times be considered unfeasible. This thesis proposes the development of a method in which internal data traffic within the system is compared to a known golden device through the use of machine learning techniques. The technique utilized in this research will be One Class Support Vector Machine (OC-SVM) analysis. This is due to its ability to be trained on only one class of data for its functionality, and can separate out anomalous classes without being trained on that data. The system will be evaluated across a number of different benchmark programs, each with it’s own respective model trained on the known good system. By analyzing the results of the model, accuracy metrics can be used to indicate a models ability to separate the data from the two system states. The success of this method would pose another feasible method of counterfeit detection that wouldn’t be hindered by the limitations of current methods.  \nSignature Sheet i  \nAbstract ii  \nTable of Contents iii  \nList of Figures v  \nList of Tables vi  \n1 Introduction 1  \n1.1 Motivation ................................. 1  \n1.2 Counterfeit Identification ......................... 2  \n1.3 Multicore Processors and the Network on Chip ............. 2  \n1.4 Objective ................................. 3  \n2 Background 4  \n2.1 Counterfeit Detection Techniques .................... 4  \n2.1.1 Physical Inspection ........................ 4  \n2.1.2 Functional Testing ........................ 6  \n2.1.3 Current Limitations ....................... 8  \n2.2 Related Works ............................... 8  \n2.3 Support Vector Machines ......................... 11  \n2.3.1 Lagrange Polynomials ...................... 12  \n2.3.2 One Class Support Vector Machines ............... 16  \n2.4 Threat Model ............................... 18  \n2.5 Problem Statement ............................ 19  \n3 Methodology and Results 20  \n3.1 Process overview ............................. 20  \n3.2 Simulation Setup ...............","cbCaipzI5N4ix4Fh","https://ap.wps.com/l/cbCaipzI5N4ix4Fh","pdf",1190140,1,53,"English","en",105,"# Introduction\n## Motivation\n## Counterfeit Identification\n## Multicore Processors and the Network on Chip\n## Objective\n# Background\n## Counterfeit Detection Techniques\n## Related Works\n## Support Vector Machines\n## Threat Model\n## Problem Statement\n# Methodology and Results\n## Process overview\n## Simulation Setup\n## Approach\n## Counterfeit Cache Alterations Detection\n## Counterfeit Core Alteration Detection\n## Top Performing Program Statistics\n# Conclusion and Future Works\n## Conclusion\n## Future Works","[{\"question\":\"Why is counterfeit hardware detection increasingly difficult in modern systems?\",\"answer\":\"Growing hardware complexity makes counterfeits harder to identify, while physical inspections only detect cases that visibly manifest and electrical testing requires expensive, extensive setups.\"},{\"question\":\"What approach does the thesis propose for detecting compromised hardware?\",\"answer\":\"It proposes comparing internal data traffic against a known golden device using machine learning, with One Class Support Vector Machine (OC-SVM) trained on trusted data to flag anomalous behavior.\"},{\"question\":\"How will the proposed method be evaluated?\",\"answer\":\"The method is evaluated across multiple benchmark programs, each trained with its own model on the known-good system; accuracy metrics from model separation are used to indicate performance.\"}]","Detecting Compromised Hardware Integrity with Machine Learning in Multicore Processors | PDF",1785944937,134,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"detecting-compromised-hardware-integrity-with-machine-learning-in-multicore-processors","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detecting-compromised-hardware-integrity-with-machine-learning-in-multicore-processors/128120/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is counterfeit hardware detection increasingly difficult in modern systems?","Question",{"text":76,"@type":77},"Growing hardware complexity makes counterfeits harder to identify, while physical inspections only detect cases that visibly manifest and electrical testing requires expensive, extensive setups.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What approach does the thesis propose for detecting compromised hardware?",{"text":81,"@type":77},"It proposes comparing internal data traffic against a known golden device using machine learning, with One Class Support Vector Machine (OC-SVM) trained on trusted data to flag anomalous behavior.",{"name":83,"@type":74,"acceptedAnswer":84},"How will the proposed method be evaluated?",{"text":85,"@type":77},"The method is evaluated across multiple benchmark programs, each trained with its own model on the known-good system; 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