Predictive analysis network attack detection using  AI model with advance technique

Author(s)	Ms. Girija Nagarajan, Prof. SHARNYAA S, Ms. jayshree V
Country	India
Abstract	This paper proposes an AI-driven approach for predictive network attack detection using machine learning models such as Random Forest, Decision Tree, and Extra Trees Classifier. The system identifies and classifies cyber threats including Denial-of-Service (DoS) attacks, phishing, and unauthorized intrusions. Advanced preprocessing techniques—such as normalization, feature selection, and handling imbalanced data—are applied to improve detection accuracy. The combination of tree-based models enables efficient handling of complex data patterns and enhances the detection of both known and emerging threats. The proposed approach provides a scalable and accurate solution for strengthening network security and improving resilience against evolving cyber attacks.
Keywords	Cybersecurity, Machine Learning, Network Attack Detection, Predictive Analytics, Random Forest, Decision Tree, Extra Trees Classifier, Data Preprocessing
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-01

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