GAN AI for Predictive Threat Detection with Explainable Risk Insights

An AI-based approach for predicting and analysis cyber-attacks using a mixture of advanced machine and deep learning techniques. Using a dataset having 40,000 cyber-attacks, we aim to detect attack from its primary symptoms before it affect system, calculating it's impact score, predicting attack and the appropriate response, severity levels, anomaly scores, and detect unauthorized users. We apply preprocessing methods like one-hot encoding, standardization, and feature selection using the Mean Decrease Gini impurity method. Key algorithms implemented include XGBoost, and Generative AI (GEN AI) to simulate potential attack scenarios. Explainable AI method such as SHAP are used to ensure model are transperant and trustworthy. Predicting cyber-attack based on primary symptoms before it affects the system using XGBoost model gives the good accuracy. The results demonstrate that our approach, with XGBoost and synthetic data from GEN AI, achieves a 99.17% accuracy. The use of GAN make model Dynamic improve scalability and use of log loss, Softmax evaluation matrix improves XGBoost performance.