Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques
With the rapid expansion of global internet connectivity, the necessity for sophisticated digital defense systems has never been more urgent. This project introduces a state-of-the-art Intrusion Detection System (IDS) designed to monitor network traffic and identify potential security breaches with high precision. By utilizing advanced machine learning architectures, our system successfully categorizes network activities into safe or malicious categories, specifically targeting well-known threats such as Denial of Service (DoS), Remote to Local (R2L), User to Root (U2R), and Probing.
Our methodology leverages the power of Python and the Flask web framework to create a seamless, interactive user experience. Unlike traditional security models, this research implements and compares three high-performance algorithms: CatBoost, ExtraTree, and Gradient Boosting. By training on the extensive KDD benchmark dataset, which contains nearly half a million traffic instances, we refined the detection process by focusing on 13 essential network features (such as source bytes, protocol types, and connection counts).
The experimental results demonstrate a significant leap in performance, with our top-tier classifiers reaching an impressive 99.9% success rate in identifying threats. This project serves as a robust framework for real-time cybersecurity monitoring, offering a scalable solution for modern organizations to safeguard their data infrastructure against evolving cyber risks.