[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120754-en":3,"doc-seo-120754-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120754,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","Hardware Trojan Detection in Chips - Removing Dependencies Between Features in Machine Learning","Growing demand for System on Chip (SoC) applications, medical implants, and IoT devices increases reliance on outsourced integrated circuit design, fabrication, and testing by untrusted third parties. This outsourcing creates opportunities for adversaries to compromise device integrity by inserting Hardware Trojans (HTs) that alter functionality, degrade performance, or open backdoors for malicious modifications. The presented approach uses supervised and unsupervised machine learning on post-synthesis netlist features while removing interdependence between features to reduce overfitting. Results show 99.2% detection with high F-measure for supervised learning and 99.5% true positive rate for an unsupervised random projection method.","Boise State University  \nScholarWorks  \n\n| Research Computing Days 2023 | Research Computing Days |\n| --- | --- |\n\n3-28-2023  \nHardware Trojan Detection in Chips by Removing Dependencies Between Features in Machine Learning  \nAlfred Moussa  \nBoise State University  \nNader Rafla  \nBoise State University  \nHardware Trojan Detection in Chips by Removing Dependencies Between Features in Machine Learning  \nAbstract  \nGlobally, there has been an increase in demand for System on Chip (SoC) applications, active medical implants, and Internet of Things (IoT) devices. However, due to challenges in the global supply chain, the design, fabrication, and testing of Integrated Circuits are often outsourced to untrusted third-party entities around the world rather than a single trusted entity. This situation presents an opportunity for adversaries to compromise the device's integrity, performance, and functionality by inserting malicious modifications known as Hardware Trojans (HTs) into the original design. HTs can also create a \"backdoor\" in the system for malicious alterations.  \nIn this research, a solution to the issue of hardware trojan is presented through the utilization of machine learning models that rely on supervised and unsupervised learning. The proposed method involves providing the netlist features of the digital hardware design post-synthesis to the machine learning model and removing any interdependence between features to prevent overfitting of the training dataset. The supervised model showed a 99.2\\% true positive and true negative rate, as well as an F-measure of  \n99.3\\%, while the unsupervised model achieved a 99. 5\\% true positive rate with the use of random projection, thereby offering a more resilient machine learning-based method for detecting hardware trojans.  \nThis student presentation is available at ScholarWorks: [https://scholarworks.boisestate.edu/rcd_2023/1](https://scholarworks.boisestate.edu/rcd_2023/1)1  \nHardware Trojan Detection in Chips by removing dependencies between features in Machine Learning  \nAlfred Moussa 1 , Dr . Nader Rafla 2  \n1Boise State University, Boise, ID, 83725 USA.  \nIntroduction  Machine Learning Models for HT Detection   \n1. Globally, there has been increase in demands for system on Chip (SoC) applications, active medical implants and Internet of Things (IoT) .  \n2. Due to the global supply chain challenges, Integrated Circuits processes of design, fabrication, and testing were outsourced to various untrusted third-party entities around the world instead of using a single trusted entity.  \nIC Development Phase  \n3 .Hardware Trojan (HT) is a malicious modification of an Integrated Circuit (IC) intended to leak sensitive information, change the functionality of a system, degrade the performance, cause a denial-of-service (DoS), or leave a backdoor to the whole system .  \nStep 2: Feature Extraction  \nThe genus tool from Cadence was used to generate multiple reports that define the timing, power, and area of the design in an output as shown in Figures below .  \nStep 3A.2: Data Shuffling-It is imperative to shuffle datasets during training to prevent the model from learning a definitive pattern .  \nStep 3A.3 MinMaxScaler: The estimator scales and translates each feature individually such that it is within the range (0, 1) on the training set.  \nStep 3A.4 Random Forest classifier - A random forest classifier is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control overfitting.  \nStep 3.B Unsupervised machine learning model  \nStep 3B. 1 Removing Labels - The unsupervised learning model doesn't use labels to identify patterns . Therefore, insights tend to be less biased when they are removed from  \nBasic structure of a Hardware Trojan  \nA typical Hardware Trojan consists of a trigger and payload circuit. Trigger monitors a rare call (signals) from the circuit and transforms u","cbCaigBEhVmAhFqk","https://ap.wps.com/l/cbCaigBEhVmAhFqk","pdf",1627511,1,3,"English","en",105,"# Abstract\n## Problem Background: Outsourced IC Supply Chain and HT Risks\n## Proposed Method: Feature Decoupling for Supervised and Unsupervised Learning\n## Model Pipeline: Feature Extraction, Scaling, Shuffling, Dropping Labels\n## Unsupervised Components: Random Projection and Random Forest\n## Evaluation: Confusion Matrix and Performance Metrics","[{\"question\":\"Why is hardware trojan detection important in outsourced integrated circuit development?\",\"answer\":\"Outsourcing design, fabrication, and testing to untrusted third parties allows adversaries to insert Hardware Trojans into the original design, enabling malicious alterations, performance degradation, or backdoors.\"},{\"question\":\"What data is fed into the machine learning models in the proposed approach?\",\"answer\":\"The method uses netlist features from the digital hardware design after synthesis as input to supervised and unsupervised machine learning models.\"},{\"question\":\"How does removing dependencies between features help the detection model?\",\"answer\":\"Removing interdependence between features prevents the model from learning definitive patterns specific to the training dataset, reducing overfitting and improving generalization.\"}]","Hardware Trojan Detection in Chips - Removing Dependencies Between Features in Machine Learning | PDF",1785731856,8,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hardware-trojan-detection-in-chips-removing-dependencies-between-features-in-machine-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"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":21},"https://docshare.wps.com/document/technology/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/hardware-trojan-detection-in-chips-removing-dependencies-between-features-in-machine-learning/120754/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is hardware trojan detection important in outsourced integrated circuit development?","Question",{"text":75,"@type":76},"Outsourcing design, fabrication, and testing to untrusted third parties allows adversaries to insert Hardware Trojans into the original design, enabling malicious alterations, performance degradation, or backdoors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data is fed into the machine learning models in the proposed approach?",{"text":80,"@type":76},"The method uses netlist features from the digital hardware design after synthesis as input to supervised and unsupervised machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"How does removing dependencies between features help the detection model?",{"text":84,"@type":76},"Removing interdependence between features prevents the model from learning definitive patterns specific to the training dataset, reducing overfitting and improving generalization.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]