Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 44 for “"Threat Detection"”.
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A fast and adaptive threat detection and prevention architecture
The late detection of security threats causes a significant increase in the risk of irreparable damages, disabling any defense attempt. We propose a fast and adaptive Threat Detection and Prevention Architecture based on stream processing and machine learning algorithms. The proposed architecture …
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USING DCGAN TO GENERATE SYNTHETIC PACKET FLOWS FOR THREAT DETECTION
… the training of machine learning models used in threat detection. Such traffic is scarce and often imbalanced as the labeling is intensive and requires domain expertise. Deep Convolutional Generative Adversarial Networks (DCGAN) are known for their image recognition and generation capabilities by …
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Threat Detection in Program Execution and Data Movement: Theory and Practice
… are one of the oldest and fundamental cyber threats. They compromise the confidentiality of data, the integrity of program logic, and the availability of services. This threat becomes even severer when followed by other malicious activities such as data exfiltration. The integration of …
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A Multi-Agent Approach to Advanced Persistent Threat Detection in Networked Systems
Advanced cyber threats that are well planned, funded and stealthy are an increasing issue facing secure networked systems. As our reliance on protected networked systems continues to grow, the motivation for developing new malicious techniques that cannot be easily detected by traditional …
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Blockchain-Based Zero Trust Threat Detection Framework for the Finance Industry Network
The rapid evolution of cyber threats has made perimeter-based security models inadequate, especially in the financial sector, where data integrity, confidentiality, and regulatory compliance are critical. Despite advances, existing frameworks like Zero Trust still face challenges such as …
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The influence of real-world factors on threat detection performance in airport X-ray screening
The visual search task carried out by X-ray screening personnel has begun to be investigated in a number of recent experiments. The goal of the present thesis was, therefore, to extend previous examinations of the factors that may be detrimental to screener performance, to understand those factors …
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A fast state-estimation-based data integrity threat detection approach for combined AC-DC bulk power systems
… a fact of life. The types of cyber attacks that threat agents can perform are varied and include false data injection and data integrity attacks, spoofing and denial of service. While it is advisable to include information technology-based intrusion detection/prevention techniques to parse and …
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Replay and bogus information attacks : simulation and empirical validation of machine learning-based cybersecurity threat detection in connected and autonomous vehicles
… challenges, especially from sophisticated threats such as replay and bogus information attacks. While international standards (e.g., IEEE 1609.2, ETSI TS 103 097) establish baseline security through authentication, encryption, and digital signatures, these controls primarily address …
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Activity-Based Target Acquisition Methods for Use in Urban Environments
… the urban environment, the soldier is subject to threatening attacks not only from the organized army but also from civilians who harbor hostility. U.S. enemies use the civilian crowd as an unconventional tactic to blend in and look like civilians, and in response to this growing trend, soldiers …
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Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification
Concealed threat detection is at the heart of critical security systems designed to en- sure public safety. Currently, methods for threat identification and detection are primarily manual, but there is a recent vision to automate the process. Problematically, developing computer vision models …
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Visual attention and cognitive biases to threat in anxiety
… is characterised by selective attention to threat (e.g., Mogg & Bradley, 1998), impaired attentional control (Eysenck, Derakshan, Santos & Calvo, 2007) and/or hypervigilance and enhanced threat detection (Eysenck, 1992).<br/><br/>This thesis utilised eye movement and reaction time measures …
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A Distributed and Hybrid AI-Based Security Framework for 5G Real-time Applications
… testbed that serves as a distributed intrusion detection system (IDS), and the implementation of a hybrid deep reinforcement learning (HDRL) method. LEMDA represents a breakthrough in data processing for IoMT systems. By intelligently reducing data complexity, LEMDA enhances the speed and …
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Design and development of a vulnerability detection framework using artificial intelligence for embedded systems
… 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 …
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Variations in Time-Dependent Mosquito-Host Interactions Across Aedes Species
… in both circadian rhythms, host seeking, and threat detection, we compared a nocturnal mosquito (Aedes japonicus) and a diurnal mosquito (Aedes aegypti). We introduced a looming visual stimulus in an LED arena and found Aedes aegypti to be more responsive to the looming stimulus than Aedes …
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Explainable Data Driven Anomaly Detection for Securing Cyber Physical Systems: Theory and Experiment
… including network segmentation and rule-based threat detection, are often inadequate against a continuously evolving attack corpus. While AI-based threat detection has gained traction and trust across various industries, most solutions rely on supervised anomaly detection methods. Although …
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A supervised machine learning-based framework to detect low-level fault injections in software systems
… vectors without software modifications. Attack detection methods utilize system-specific software features and unsupervised learning due to lack of labelled data. Unsupervised pattern recognition is vulnerable to false data injection, and Machine Learning algorithms such as Artificial and …
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Artificial Intelligence for Cyber Security Threats
… digital landscape becomes more intricate, cyber threats continue to advance in sophistication and scale, demanding proactive and adaptive solutions. This thesis delves into the realm of Artificial Intelligence (AI) and its transformative impact on bolstering cybersecurity defenses against a …
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Reliable Bandwidth Conservative Queue-End Detection and Warning System Using Smart Phone Collaboration Techniques
… of the major components of public safety are: threat detection and notification. This thesis provides threat detection through sensor networks, collaboration of devices, and notification through three types of warning messages using cell broadcast technology. Most emergency conditions, be it …
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IoT network Malicious Behaviour Profiling Based on Explainable AI Using LSTM and SHAP
… connectivity but exposed networks to new cyber threats, particularly from botnets. Detecting and identifying malicious data is critical for early threat detection, understanding botnet attack patterns, and deploying countermeasures. This research proposes an IoT Bot detection and identification …
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Insider threat simulation and performance analysis of insider detection algorithms with role based models
Insider threat problems are widespread in industry today. They have resulted in huge losses to organizations. The security reports by leading organizations point out the fact that there have been many more insider attacks in recent years than any other form of attack. Detection of these insider …
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