Real-Time Cyber Threat Detection and Response System

The Real-Time Cyber Threat Detection and Response System is an intelligent security framework designed to proactively identify, analyze, and respond to cyber threats in real time. With the growing sophistication of cyberattacks targeting critical infrastructure, traditional static defense mechanisms are no longer sufficient. This system addresses that gap by leveraging machine learning algorithms and dimensionality reduction techniques such as Principal Component Analysis (PCA) to enable accurate and efficient threat detection. The system captures input data, such as network traffic or log features, and processes it through a trained machine learning model to classify instances as normal or malicious. A pre-trained model, stored using Python's pickle module, evaluates the processed input, while PCA ensures that irrelevant or redundant features are minimized to boost performance and reduce computation time. The application is deployed as a web-based interface using Flask, providing functionalities like secure data upload, dataset preview, performance visualization, and threat prediction. Users can interact with the system in real time, upload network traffic datasets, and receive instant feedback on potential threats. The platform is built to support automated response mechanisms, which can be extended to alert generation, logging, or blocking malicious activities.