Intruders' Behavior Unveiled: A Dual-Tier Behavior-driven Model for Malicious Activity Detection in IoT Network Using Graph Learning

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dc.contributor.advisorHabibi Lashkari, Arash
dc.contributor.authorShafi, MohammadMoein
dc.date.accessioned2025-04-10T10:50:26Z
dc.date.available2025-04-10T10:50:26Z
dc.date.copyright2024-12-02
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:50:26Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractIn recent years, IoT technology has transformed smart homes, with most households now including several IoT devices that provide convenience and automation. However, the security of these smart homes is paramount, as vulnerabilities can expose residents to risks like unauthorized access, data breaches, and operational disruption. Network-based threats pose a particularly critical risk due to the numerous vulnerabilities in wireless communication between devices, making it possible for attackers to intercept data or do malicious activities. While traditional intrusion detection systems exist, they are often ineffective in detecting zero-day attacks and lack the ability to identify malicious patterns across diverse threat scenarios due to limited diversity in their detection models. Moreover, these systems are not designed to fully detect all types of intrusions, especially those involving both external network activities and internal IoT communications among smart home devices. This gap is made worse by the challenges in creating specialized IoT datasets that cover a diverse set of malicious activities and data types, which require extensive technical knowledge, a diverse range of devices, and expertise in capturing, executing, and labeling attack scenarios. Such datasets are crucial for data-driven intrusion detection systems. Addressing these challenges, this thesis introduces a dual-tier detection system that effectively can zero-day attacks, and is designed in a way to be scalable for learning the behavior of diverse malicious activities. the proposed solution leverages data from both the smart home hub’s internet connection and the internal network communication of IoT devices to detect and profile malicious activities using a novel graph learning approach. Furthermore, to support this research, we have created the largest IoT smart home dataset, incorporating real-world data from over 50 devices and more than 100 carefully designed attack scenarios, captured over a five-month period. The analysis of this dataset and the performance of our detection model demonstrate promising results, providing a valuable resource and foundation for advancing smart home IoT security.
dc.identifier.urihttps://hdl.handle.net/10315/42816
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsInternet of Things (IoT)
dc.subject.keywordsSmart home security
dc.subject.keywordsIoT vulnerabilities
dc.subject.keywordsNetwork-based threats
dc.subject.keywordsWireless communication security
dc.subject.keywordsIoT device-to-device communication
dc.subject.keywordsIntrusion detection systems (IDS)
dc.subject.keywordsZero-day attack detection
dc.subject.keywordsMulti-tier detection model
dc.subject.keywordsIoT threat profiling
dc.subject.keywordsGraph-based learning
dc.subject.keywordsIoT network traffic analysis
dc.subject.keywordsZ-wave protocol security
dc.subject.keywordsIoT wireless protocols
dc.subject.keywordsWi-Fi
dc.subject.keywordsZigbee
dc.subject.keywordsBluetooth
dc.subject.keywordsZ-Wave
dc.subject.keywordsDataset for IoT security
dc.subject.keywordsSmart home testbed development
dc.subject.keywordsIoT attack scenario design
dc.subject.keywordsReal-world IoT dataset
dc.subject.keywordsMalicious activity detection
dc.subject.keywordsIoT traffic monitoring
dc.subject.keywordsIoT data labeling and analysis
dc.subject.keywordsIoT dataset benchmarking
dc.subject.keywordsIoT device behavior analysis
dc.subject.keywordsSmart home automation risks
dc.subject.keywordsIoT privacy concerns
dc.subject.keywordsIoT device taxonomy
dc.subject.keywordsIoT communication patterns
dc.subject.keywordsIoT security challenges
dc.subject.keywordsIoT research frameworks
dc.subject.keywordsIoT threat landscape
dc.subject.keywordsIoT network anomalies
dc.subject.keywordsIoT ecosystem security
dc.subject.keywordsSmart home threat detection
dc.subject.keywordsIoT attack simulation
dc.subject.keywordsAdvanced IoT security solutions
dc.subject.keywordsIoT traffic analysis
dc.subject.keywordsIoT data capture techniques
dc.subject.keywordsIoT security evaluation
dc.subject.keywordsIoT cybersecurity
dc.subject.keywordsIoT attack detection
dc.subject.keywordsIoT defense mechanisms
dc.subject.keywordsIoT behavioral analytics
dc.subject.keywordsScalable IoT security model
dc.subject.keywordsIoT dataset creation challenges
dc.subject.keywordsIoT intrusion detection
dc.subject.keywordsIoT network forensics
dc.subject.keywordsIoT device interoperability security
dc.subject.keywordsEmerging IoT threats
dc.subject.keywordsIoT threat intelligence
dc.subject.keywordsIoT security best practices
dc.subject.keywordsIoT Detection algorithm design
dc.subject.keywordsIoT ecosystem resilience
dc.subject.keywordsIoT data-driven security model
dc.subject.keywordsSmart office security
dc.subject.keywordsSmart office networks
dc.subject.keywordsSmart home automation security
dc.subject.keywordsSmart office automation
dc.subject.keywordsIoT-enabled smart offices
dc.subject.keywordsSmart office vulnerabilities
dc.subject.keywordsSmart home device security
dc.subject.keywordsSmart office device management
dc.subject.keywordsIoT in smart workspaces
dc.subject.keywordsSmart building cybersecurity
dc.subject.keywordsIoT-driven smart environments
dc.subject.keywordsSmart office data privacy
dc.subject.keywordsIoT-based smart infrastructure
dc.subject.keywordsSmart home energy efficiency risks
dc.subject.keywordsIoT in smart office design
dc.subject.keywordsIoT threats in smart workplaces
dc.subject.keywordsCollaborative IoT workspaces
dc.subject.keywordsCybersecurity for smart offices
dc.subject.keywordsSmart home network architecture
dc.subject.keywordsSecure smart office solutions
dc.subject.keywordsAutomation risks in smart environments
dc.subject.keywordsIoT connectivity in smart homes and offices
dc.subject.keywordsSmart environment intrusion detection
dc.subject.keywordsIoT in smart home monitoring systems
dc.titleIntruders' Behavior Unveiled: A Dual-Tier Behavior-driven Model for Malicious Activity Detection in IoT Network Using Graph Learning
dc.typeElectronic Thesis or Dissertation

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