[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128202-en":3,"doc-seo-128202-105":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},128202,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","HARDWARE TROJAN DETECTION IN OPEN-SOURCE HARDWARE DESIGNS USING MACHINE LEARNING","Globalization of the hardware supply chain lowers costs while increasing security risks, including the possible insertion of hardware trojans by third parties. Traditional detection techniques often struggle to scale because they rely on limited, simple examples such as AES. Despite the transparency offered by open-source hardware, security is not guaranteed. This research applies Natural Language Processing and Machine Learning to detect trojans in complex designs such as RISC-V, using benchmark data and LLM-generated synthetic datasets.","VICTOR TAKASHI HAYASHI  \nHARDWARE TROJAN DETECTION IN OPEN-SOURCE HARDWARE DESIGNS USING  \nMACHINE LEARNING  \nS˜ao Paulo  \n2025  \nVICTOR TAKASHI HAYASHI  \nHARDWARE TROJAN DETECTION IN OPEN-SOURCE HARDWARE DESIGNS USING  \nMACHINE LEARNING  \nDocumento apresentado `a Escola Polit´ecnica da Universidade de S˜ao Paulo para para obten¸c˜ao do T´ıtulo de Doutorem Ciˆencias.  \nS˜ao Paulo  \nVICTOR TAKASHI HAYASHI  \nHARDWARE TROJAN DETECTION IN OPEN-SOURCE HARDWARE DESIGNS USING  \nMACHINE LEARNING  \nVers˜ao Corrigida  \nDocumento apresentado `a Escola Polit´ecnica da Universidade de S˜ao Paulo para para obten¸c˜ao do T´ıtulo de Doutorem Ciˆencias.  \n´Area de Concentra¸c˜ao:  \nEngenharia da Computa¸c˜ao  \nOrientador:  \nProf. Dr. Wilson Vicente Ruggiero  \nS˜ao Paulo  \nAutorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meioconvencional ou eletrônico, para fins de estudo e pesquisa, desde que citada a fonte.  \nEste exemplar foi revisado e alterado em relação à versão original, sob responsabilidade única do autor e com a anuência de seu orientador.  \nSão Paulo, 31 de março de 2025  \nAssinatura do autor  \nAssinatura do orientador  \nCatalogação-na-publicação  \nHayashi --versão corr. , Victor Takashi  \nHardware Trojan Detection in Open-source Hardware Designs using Machine Learning / V. T. Hayashi --versão corr. --São Paulo, 2025.  \n145 p.  \nTese (Doutorado) -Escola Politécnica da Universidade de São Paulo. Departamento de Engenharia de Computação e Sistemas Digitais.  \n1.Segurança 2. Hardware 3.Aprendizado de Máquina 4. Processamento de Linguagem Natural 5.Código Aberto I. Universidade de São Paulo. Escola Politécnica. Departamento de Engenharia de Computação e Sistemas Digitais II.t.  \nAGRADECIMENTOS  \nAgrade¸co `a minha fam´ılia por todo o apoio durante minha educa¸c˜ao, especialmenteao apoio da minha esposa Carolina.  \nAo Prof. Dr. Wilson Ruggiero, pela orienta¸c˜ao na minha forma¸c˜ao como pesquisador. Ao Prof. Dr. Reginaldo Arakaki, com quem compartilhei diversas publica¸c˜oes.  \n“This compass points towards what you want most. Never betray it.”  \n--Pirates of the Caribbean  \nRESUMO  \nA globaliza¸c˜ao da cadeia de suprimentos de hardware reduz custos, mas aumenta os desafios de seguran¸ca com a poss´ıvel inser¸c˜ao de hardware trojans por terceiros. M´etodostradicionais de detec¸c˜ao apresentam limita¸c˜oes de escalabilidade ao usar apenas exemplos simples (e.g., AES) . Embora o hardware de c´odigo aberto promova transparˆencia, ele n˜ao garante seguran¸ca. Nesta pesquisa, t´ecnicas de Processamento de Linguagem Natural (PLN) e Machine Learning (ML) foram aplicadas para identificar hardware trojans em designs complexos (e.g., RISC-V) . Usando dados de benchmarks existentes (ISCAS85-89, TrustHub) e dados sint´eticos gerados com Large Language Models (LLM), foi utilizado um conjunto de 3808 instˆancias nesta pesquisa. A abordagem com TF-IDF e Decision Tree alcan¸cou 97,26% de acur´acia com este conjunto de dados, superando o estado da arte. O uso de LLMs com prompt optimization atingiu recall de 99%, minimizando falsos negativos. Como principais contribui¸c˜oes, foi desenvolvido um novo framework integrando PLN, ML e LLMs para aumentar a seguran¸ca em hardwares de c´odigo aberto, contemplando a gera¸c˜ao e detec¸c˜ao de hardware trojans complexos e os conjuntos de dados abertos resultantes.  \nPalavras-Chave – seguran¸ca, hardware, aprendizado de m´aquina, processamento de linguagem natural, c´odigo aberto.  \nABSTRACT  \nThe globalization of the hardware supply chain reduces costs but increases security challenges with the potential insertion of hardware trojans by third parties. Traditional detection methods face scalability limitations by relying solely on simple examples (e.g. , AES) . Although open-source hardware promotes transparency, it does not guarantee security. In this research, Natural Language Processing (NLP) and Machine Learning (ML) techniques were applied to identify hardware trojans in complex de","cbCaisheDGoib8l5","https://ap.wps.com/l/cbCaisheDGoib8l5","pdf",4478181,1,145,"English","en",105,"# Abstract\n## Research Problem and Motivation\n## Proposed Approach (NLP + ML + LLM)\n## Datasets and Experimental Results\n## Contributions\n# List of Figures\n## Hardware Trojan Taxonomy\n## Detection Models and Metrics\n## Trojan Generation Using LLMs","[{\"question\":\"Why do hardware trojans remain a security concern even for open-source hardware designs?\",\"answer\":\"Open-source hardware improves transparency but does not inherently prevent malicious modifications. Third parties can still insert hardware trojans during the supply chain process.\"},{\"question\":\"What techniques are used to detect hardware trojans in this research?\",\"answer\":\"The approach integrates Natural Language Processing with Machine Learning, and leverages Large Language Models with prompt optimization for improved detection performance.\"},{\"question\":\"How are the datasets for model training and evaluation constructed?\",\"answer\":\"The study uses existing benchmarks such as ISCAS85-89 and TrustHub, and supplements them with synthetic data generated using Large Language Models, resulting in a dataset of 3,808 instances.\"}]","HARDWARE TROJAN DETECTION IN OPEN-SOURCE HARDWARE DESIGNS USING MACHINE LEARNING | PDF",1785945533,365,{"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},"hardware-trojan-detection-in-open-source-hardware-designs-using-machine-learning","",{"@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/hardware-trojan-detection-in-open-source-hardware-designs-using-machine-learning/128202/",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","2026-08-25","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 do hardware trojans remain a security concern even for open-source hardware designs?","Question",{"text":76,"@type":77},"Open-source hardware improves transparency but does not inherently prevent malicious modifications. Third parties can still insert hardware trojans during the supply chain process.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What techniques are used to detect hardware trojans in this research?",{"text":81,"@type":77},"The approach integrates Natural Language Processing with Machine Learning, and leverages Large Language Models with prompt optimization for improved detection performance.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the datasets for model training and evaluation constructed?",{"text":85,"@type":77},"The study uses existing benchmarks such as ISCAS85-89 and TrustHub, and supplements them with synthetic data generated using Large Language Models, resulting in a dataset of 3,808 instances.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]