[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123231-en":3,"doc-seo-123231-105":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":4,"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},123231,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","SeniorSentry - Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning","SeniorSentry addresses the growing need to secure IoT-enabled sensors used for aging in place by detecting attacks and other abnormal behavior. The work verifies statistical correlations among co-located sensors using two public smart-home datasets and a laboratory-collected dataset. It then builds a contextual anomaly detection model with a sliding-window approach and supervised machine learning, achieving a true positive rate of 89.47% with a 0% false positive rate. Finally, it targets BrakTooth threats by proposing a cost-effective active sniffer and supervised detection for Bluetooth Classic stack layers.","SeniorSentry: Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning  \nby  \nAchyuth Nandikotkur  \nB.Tech., Manipal Institute of Technology, 2017  \nA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of  \nMASTER OF APPLIED SCIENCE  \nin the Department of Electrical and Computer Engineering  \n© Achyuth Nandikotkur, 2023 University of Victoria  \nAll rights reserved. This thesis may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author.  \nii  \nSeniorSentry: Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning  \nby  \nAchyuth Nandikotkur  \nB.Tech., Manipal Institute of Technology, 2017  \nSupervisory Committee  \nDr. Issa Traore, Supervisor  \n(Department of Electrical and Computer Engineering)  \nDr. Mihai Sima, Department Member  \n(Department of Electrical and Computer Engineering)  \niii  \nSupervisory Committee  \nDr. Issa Traore, Supervisor  \n(Department of Electrical and Computer Engineering)  \nDr. Mihai Sima, Department Member  \n(Department of Electrical and Computer Engineering)  \nABSTRACT  \nWith the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious activities by detecting attacks or other anomalies. In this work, we first confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our lab. Then, we leverage the sliding window approach and supervised machine learning to develop a novel contextual-anomaly-detection model that reaches a true positive rate of 89 .47% and a false positive rate of 0% . Furthermore, as homes become smarter with these IoT sensors, the underlying communication technology they employ becomes a target for attackers. Typically, these sensors are paired with a micro-controller that has an inbuilt communication module (e.g. , Bluetooth/WiFi), to form an edge device that facilitates communication. Monitoring vitals, climate control, illumination control, fall detection, incontinence detection, pill dispensing, and several other functions are successfully addressed by these devices. The family of vulnerabilities recently found in the the Link Manager Protocol (LMP) and baseband layers of the Bluetooth Classic (BT Classic) stack called BrakTooth, poses a genuine threat to the availability of such devices. In response, our research introduces a costeffective experimental active sniffer that captures traffic at both these layers of the BT Classic stack and utilizes supervised machine learning to detect Braktooth-based attacks.  \niv  \nContents  \nSupervisory Committee ii  \nAbstract iii  \nContents iv  \nList of Tables vii  \nList of Figures viii  \nAcknowledgements x  \nDedication xi  \n1 Introduction 1  \n1.1 Context .................................. 1  \n1.1.1 What is aging in place? ...................... 1  \n1.1.2 How can technology support aging in place? .......... 1  \n1.1.3 Threats to AgeTech ........................ 2  \n1.2 Research Problem ............................. 4  \n1.3 Research Questions ............................ 5  \n1.4 Thesis contributions ........................... 6  \n1.5 Thesis outline ............................... 7  \n2 Background and Related Work 8  \n2.1 Anomaly detection in IoT sensor data .................. 8  \n2.1.1 Background ............................ 8  \n2.1.2 Related Works .......................... 10  \n2.2 Detection of Bluetooth attacks ...................... 13  \n2.2.1 Background ............................ 13  \nv  \n2.2.2 Related Works .......................... 16  \n2.3 Summary ................................. 18  \n3 Contextual Anomaly Detection Framework 19  \n3.1 Datasets .................................. 19  \n3.1.1 Open Smart Home Dataset ................... 19  \n3.1.2 Smart Building Dataset ..................... 20  \n3.1.3 ISOT AgeTech Dataset ..","cbCaiuPYsaNJ3TNY","https://ap.wps.com/l/cbCaiuPYsaNJ3TNY","pdf",2295219,1,69,"English","en",105,"# Introduction\n## Context\n## Research Problem\n## Research Questions\n## Thesis contributions\n## Thesis outline\n# Background and Related Work\n## Anomaly detection in IoT sensor data\n## Detection of Bluetooth attacks\n## Summary\n# Contextual Anomaly Detection Framework\n## Datasets\n## Proposed Detection Model\n## Summary\n# BrakTooth Attack Detection\n## Experimental Setup\n## Summary\n# Experimental Results and Discussion\n## Evaluation Metrics\n## Contextual Anomaly Detection in IoT\n## BrakTooth Attack Detection\n# Conclusions and Future Work\n## Summary\n## Broader Implications for IoT Security and Future Work","[{\"question\":\"How does SeniorSentry detect anomalies in co-located AgeTech sensors?\",\"answer\":\"It statistically analyzes correlations between sensors, then applies a sliding-window approach with supervised machine learning to build a contextual anomaly detection model.\"},{\"question\":\"What performance does the contextual anomaly detection model achieve?\",\"answer\":\"The model reaches a true positive rate of 89.47% and a false positive rate of 0%.\"},{\"question\":\"How does the thesis detect BrakTooth-based attacks on Bluetooth Classic devices?\",\"answer\":\"It introduces a cost-effective experimental active sniffer that captures traffic at the Link Manager Protocol (LMP) and baseband layers, then uses supervised machine learning to identify BrakTooth-based attacks.\"}]","SeniorSentry - Safeguarding AgeTech Devices and Sensors Using Contextual Anomaly Detection and Supervised Machine Learning | PDF",1785815354,174,{"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},"seniorsentry-safeguarding-agetech-devices-and-sensors-using-contextual-anomaly-detection-and-supervised-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@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/seniorsentry-safeguarding-agetech-devices-and-sensors-using-contextual-anomaly-detection-and-supervised-machine-learning/123231/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does SeniorSentry detect anomalies in co-located AgeTech sensors?","Question",{"text":75,"@type":76},"It statistically analyzes correlations between sensors, then applies a sliding-window approach with supervised machine learning to build a contextual anomaly detection model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What performance does the contextual anomaly detection model achieve?",{"text":80,"@type":76},"The model reaches a true positive rate of 89.47% and a false positive rate of 0%.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis detect BrakTooth-based attacks on Bluetooth Classic devices?",{"text":84,"@type":76},"It introduces a cost-effective experimental active sniffer that captures traffic at the Link Manager Protocol (LMP) and baseband layers, then uses supervised machine learning to identify BrakTooth-based attacks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"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":53,"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]