Network Surveillance System Using Machine Learning

The exponential proliferation of Internet of Things (IoT) ecosystems has precipitated unprecedented cybersecurity vulnerabilities, necessitating sophisticated automated surveillance mechanisms capable of perpetual network traffic scrutiny. This research presents an “Intelligent Network Surveillance System” that addresses the critical limitations of conventional signature-based intrusion detection methodologies through the implementation of advanced machine learning paradigms. Traditional network monitoring systems, characterized by static threshold parameters and rule-based algorithmic frameworks, demonstrate insufficient adaptability to evolving threat landscapes and exhibit substantial scalability constraints when confronted with heterogeneous IoT device populations. Our proposed framework integrates ensemble learning methodologies with the comprehensive IoT-23 dataset, encompassing over twenty million network flow records across diverse attack vectors and benign communication patterns. The system architecture incorporates a quaternary modular design: Data Preprocessing and Sanitization Module, Multidimensional Feature Extraction and Engineering Module, Adaptive System Training and Model Optimization Module, and Intelligent Classification and Anomaly Detection Module. Empirical evaluation demonstrates exceptional performance characteristics, achieving overall classification accuracy of 94.2%, precision weighted average of 0.923, recall weighted average of 0.918, and F1-score weighted average of 0.920.