Cyber Attack Prediction From Traditional ML to Generative Artificial Intelligence

Jaswanth Syam Sundar Garugu

doi:10.36948/ijfmr.2026.v08i04.85047

Cyber Attack Prediction From Traditional ML to Generative Artificial Intelligence

Author(s)	Jaswanth Syam Sundar Garugu
Country	India
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.
Keywords	Cyber attack prediction, Intrusion detection, Machine learning, Deep learning, Generative artificial intelligence, Explainable artificial intelligence, Ensemble learning, Network security
Field	Computer > Network / Security
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-02
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85047

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Cyber Attack Prediction From Traditional ML to Generative Artificial Intelligence

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