Cyber Attack Prediction From Traditional ML to Generative Artificial Intelligence

Abstract

The rise in frequency and complexity of cyber attacks has led to an increased demand for advanced intrusion detection systems that can effectively identify emerging network threats. This document outlines an AI-based framework for predicting cyber attacks, which incorporates machine learning, deep learning, generative artificial intelligence, and explainable artificial intelligence techniques utilizing the CICIDS2017 dataset. The dataset undergoes preprocessing, including data cleaning, feature transformation, normalization, and principal component analysis, to enhance data quality and computational efficiency. A variety of machine learning and deep learning models are assessed comparatively, while generative AI models are utilized to simulate synthetic attack patterns and improve anomaly representation. Experimental findings indicate that the ensemble Voting Classifier achieves the highest classification accuracy at 99.6%, while the LSTM model reaches 99.3%, underscoring the effectiveness of both ensemble learning and sequential deep learning in cyber attack prediction. The interpretability of the models is enhanced using LIME and SHAP, which provide clear explanations of prediction results. Additionally, a Flask-based deployment framework supports real-time network traffic classification and visualization, offering an interpretable and scalable solution for advanced cybersecurity applications.