AI-Driven Network Threat Behavior Pattern Recognition and Classification: An Ensemble Learning Approach with Temporal Analysis

Abstract

Network security landscapes demand sophisticated threat detection mechanisms capable of identifying evolving malicious behaviors across diverse attack vectors. This research develops a comprehensive machine learning framework for automated threat behavior classification through multi-dimensional feature extraction and ensemble learning methodologies. The proposed system integrates temporal analysis techniques with traditional behavioral pattern recognition, enabling dynamic threat categorization across malware, intrusion attempts, and data exfiltration scenarios. Feature engineering techniques encompass network traffic characteristics, system call patterns, and file operation sequences, processed through recursive feature elimination and information gain algorithms. Random forest and gradient boosting classifiers form the ensemble architecture, enhanced by temporal sequence modeling for predictive threat assessment. Experimental validation demonstrates 94.7% classification accuracy across five threat categories, with computational efficiency improvements of 34.2% compared to traditional single-model approaches. The framework addresses critical cybersecurity challenges by providing automated threat categorization capabilities that reduce manual analysis overhead while maintaining high precision in threat identification. Results indicate significant performance gains in multi-class threat classification tasks, establishing foundations for real-time security response systems.