Cyber Attack Detection and Prediction System

The “Cyber Attack Detection and Prediction System” is a comprehensive web-based application developed using Flask, aimed at facilitating the analysis and prediction of cyber attacks. This system enables users to upload datasets containing information pertaining to various cyber attacks, preprocess the data, and train machine learning models to detect patterns and forecast future attacks. Key features include user registration, data loading, preprocessing, model training, and prediction. The application offers a diverse selection of machine learning algorithms, including AdaBoost, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Classifier, and Logistic Regression, for robust analysis. Through an intuitive interface, users can input specific parameters related to cyber attacks and obtain predictions regarding the likelihood of an attack occurrence. This system serves as a valuable tool for cybersecurity professionals, researchers, and organizations seeking to bolster their threat detection capabilities and mitigate potential cyber risks.