{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1981"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1981","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Design and development of a vulnerability detection framework using artificial intelligence for embedded systems","abstract":"Embedded systems play a critical role in industrial control, IoT, autonomous systems, and critical infrastructure. However, their widespread adoption introduces security vulnerabilities across three layers: the application layer, the embedded OS layer, and the hardware layer. This thesis presents a comprehensive approach to vulnerability detection and mitigation across these layers, leveraging machine learning, deep learning, and AI-driven solutions. At the application layer, this research focuses on low-level programming applications that control or impact embedded systems, such as firmware, system utilities, and real-time control software. We develop deep learning-based models for automated vulnerability detection, evaluating Convolutional Neural Networks (CNNs) and BERT-based architectures. For the embedded OS layer, we introduced the EVDD dataset (Embedded Vulnerability Detection) to enhance Linux kernel vulnerability detection. Using big data processing, we construct a balanced dataset to improve machine learning models for intrusion detection and OS-level security analysis. Deep neural networks (DNNs) are implemented to detect and mitigate attacks, supporting effective threat detection under resource-constrained environments. At the hardware layer, we investigate vulnerabilities in FPGA based systems, such as bitstream manipulation and HDL coding flaws. We introduce the BitVul-LLM model for detecting FPGA bitstream vulnerabilities, and SecureLLAMA, a transformer-based framework using the FPGAvul dataset to identify hardware security risks. These models automate threat detection and significantly enhance hardware-level security. To improve detection across all layers, we developed an integrated framework using LLama 3, combining our models into a unified AI-driven system. This framework classifies vulnerabilities as hardware, OS-level, or application-level threats, improving precision and response strategies. By correlating vulnerabilities across layers, it enables advanced security assessment, efficient threat mitigation, and proactive defense mechanisms.","abstract_html":"Embedded systems play a critical role in industrial control, IoT, autonomous systems, and critical infrastructure. However, their widespread adoption introduces security vulnerabilities across three layers: the application layer, the embedded OS layer, and the hardware layer. This thesis presents a comprehensive approach to vulnerability detection and mitigation across these layers, leveraging machine learning, deep learning, and AI-driven solutions. At the application layer, this research focuses on low-level programming applications that control or impact embedded systems, such as firmware, system utilities, and real-time control software. We develop deep learning-based models for automated vulnerability detection, evaluating Convolutional Neural Networks (CNNs) and BERT-based architectures. For the embedded OS layer, we introduced the EVDD dataset (Embedded Vulnerability Detection) to enhance Linux kernel vulnerability detection. Using big data processing, we construct a balanced dataset to improve machine learning models for intrusion detection and OS-level security analysis. Deep neural networks (DNNs) are implemented to detect and mitigate attacks, supporting effective threat detection under resource-constrained environments. At the hardware layer, we investigate vulnerabilities in FPGA based systems, such as bitstream manipulation and HDL coding flaws. We introduce the BitVul-LLM model for detecting FPGA bitstream vulnerabilities, and SecureLLAMA, a transformer-based framework using the FPGAvul dataset to identify hardware security risks. These models automate threat detection and significantly enhance hardware-level security. To improve detection across all layers, we developed an integrated framework using LLama 3, combining our models into a unified AI-driven system. This framework classifies vulnerabilities as hardware, OS-level, or application-level threats, improving precision and response strategies. By correlating vulnerabilities across layers, it enables advanced security assessment, efficient threat mitigation, and proactive defense mechanisms.","abstract_has_math":false,"creators":["Alqarni, Mansour"],"institution":"University of Ontario Institute of Technology","degree_name":"Doctor of Philosophy (PhD)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01","date_published":"2025-08-01","updated_at":"2026-07-24T05:35:16Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1981","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Alqarni, Mansour"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-18T15:17:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-18T15:17:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1981"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Embedded systems play a critical role in industrial control, IoT, autonomous systems, and critical infrastructure. 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Deep neural networks (DNNs) are implemented to detect and mitigate attacks, supporting effective threat detection under resource-constrained environments. At the hardware layer, we investigate vulnerabilities in FPGA based systems, such as bitstream manipulation and HDL coding flaws. We introduce the BitVul-LLM model for detecting FPGA bitstream vulnerabilities, and SecureLLAMA, a transformer-based framework using the FPGAvul dataset to identify hardware security risks. These models automate threat detection and significantly enhance hardware-level security. To improve detection across all layers, we developed an integrated framework using LLama 3, combining our models into a unified AI-driven system. This framework classifies vulnerabilities as hardware, OS-level, or application-level threats, improving precision and response strategies. 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At the hardware layer, we investigate vulnerabilities in FPGA based systems, such as bitstream manipulation and HDL coding flaws. We introduce the BitVul-LLM model for detecting FPGA bitstream vulnerabilities, and SecureLLAMA, a transformer-based framework using the FPGAvul dataset to identify hardware security risks. These models automate threat detection and significantly enhance hardware-level security. To improve detection across all layers, we developed an integrated framework using LLama 3, combining our models into a unified AI-driven system. This framework classifies vulnerabilities as hardware, OS-level, or application-level threats, improving precision and response strategies. 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