Enhancing Cybersecurity Through Machine Learning-based Anomaly Detection in IoT Environments
Table Of Contents
<p><br>Table of Contents:<br><br>1. Introduction<br> - 1.1 Background and Motivation<br> - 1.2 Objectives of the Study<br> - 1.3 Scope and Significance<br> - 1.4 Research Questions<br> - 1.5 Methodology<br> - 1.6 Literature Review Overview<br> - 1.7 Structure of the Thesis<br><br>2. Literature Review<br> - 2.1 Evolution of Cybersecurity Threats<br> - 2.2 Role of Machine Learning in Cybersecurity<br> - 2.3 Anomaly Detection Techniques<br> - 2.4 IoT Security Challenges<br> - 2.5 State-of-the-Art Solutions in Anomaly Detection<br> - 2.6 Machine Learning Algorithms for Intrusion Detection<br> - 2.7 Ethical and Privacy Implications in Cybersecurity<br><br>3. IoT Environment and Threat Landscape<br> - 3.1 Architecture of IoT Systems<br> - 3.2 Common Threats in IoT Networks<br> - 3.3 Vulnerabilities in IoT Devices<br> - 3.4 Attack Vectors in IoT Environments<br> - 3.5 Case Studies of Cybersecurity Incidents in IoT<br> - 3.6 Regulatory Frameworks for IoT Security<br> - 3.7 Emerging Trends in IoT Security<br><br>4. Machine Learning-based Anomaly Detection<br> - 4.1 Overview of Anomaly Detection Models<br> - 4.2 Feature Engineering for IoT Anomaly Detection<br> - 4.3 Training and Evaluation of Machine Learning Models<br> - 4.4 Real-time Anomaly Detection in Dynamic IoT Environments<br> - 4.5 Ensemble Learning Approaches<br> - 4.6 Explainability and Interpretability of Anomaly Detection Models<br> - 4.7 Challenges and Future Directions in ML-based Anomaly Detection<br><br>5. Implementation and Evaluation<br> - 5.1 Design and Development of Anomaly Detection System<br> - 5.2 Integration with IoT Infrastructure<br> - 5.3 Performance Metrics for Anomaly Detection<br> - 5.4 Case Studies on Anomaly Detection Effectiveness<br> - 5.5 Economic and Practical Implications<br> - 5.6 User Interface and System Usability<br> - 5.7 Recommendations for Further Enhancements and Deployment<br><br><br></p>
Thesis Abstract
<p>Abstract<br><br>As the Internet of Things (IoT) continues to proliferate, the security challenges associated with interconnected devices become increasingly pronounced. This research endeavors to enhance cybersecurity by leveraging machine learning-based anomaly detection in IoT environments. The study encompasses a thorough review of cybersecurity threats, the pivotal role of machine learning, and contemporary anomaly detection techniques. Special emphasis is placed on understanding the intricacies of the IoT landscape, exploring common threats, vulnerabilities, and regulatory frameworks. The core of the research involves the development and evaluation of a machine learning-based anomaly detection system tailored for IoT, addressing issues of real-time detection, interpretability of models, and practical implementation challenges. The findings contribute to the ongoing discourse on bolstering cybersecurity measures in the dynamic and intricate realm of IoT.<br></p>
Thesis Overview
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