AI Based Threat Detection System

With the rapid growth of network cyber threats, there exists a growing need for advanced, scalable and highly accurate mechanisms for threat detection. AI based threat detection system is presented in this paper that uses machine learning and feature engineering techniques for classifying network traffic as normal to malicious. It leverages state of the art algorithms including Gradient Boosted Trees and a Multi-Layer Perceptron, achieving high accuracy with optimized preprocessing steps such as Principal Component Analysis and Chi Square feature selection. Learning techniques and feature engineering methods to classify network traffic as either normal or malicious. The system incorporates state-of-the-art algorithms, including Gradient Boosted Trees and Multi-Layer Perceptron achieving high accuracy through optimized preprocessing steps such as Principal Component Analysis and Chi-Square feature selection. A Flask application and Python GUI are utilized as a means to test the system via user friendly interfaces for real time prediction and validation.