Investigating Anomaly Detection Strategies in Large-Scale Industrial IoT Data Sets

1. Introduction 1.1. Background and Motivation 1.2. Objectives of the Study 1.3. Structure of the Thesis 2. Overview of Industrial IoT 2.1. Definition and Components 2.2. Applications in Industry 2.3. Importance of Data Analysis 3. Anomaly Detection in IoT 3.1. Introduction to Anomaly Detection 3.2. Types of Anomalies 3.3. Challenges in Industrial IoT 4. Data Processing Techniques 4.1. Data Preprocessing Methods 4.2. Feature Engineering Approaches 4.3. Dimensionality Reduction 5. Anomaly Detection Algorithms 5.1. Statistical Methods 5.2. Machine Learning Approaches 5.3. Deep Learning Strategies 6. Evaluation of Detection Strategies 6.1. Performance Metrics 6.2. Comparative Analysis 6.3. Case Study Scenarios 7. Implementation and Results 7.1. Experimental Setup 7.2. Results Discussion 7.3. Insights and Observations 8. Conclusion and Future Work 8.1. Summary of Findings 8.2. Limitations of the Study 8.3. Recommendations for Future Research