Cybersecurity threats have evolved significantly in complexity, stealth, and persistence, rendering traditional reactive defense mechanisms increasingly ineffective. Signature-based intrusion detection systems (IDS) detect known threats but fail against zero-day exploits and advanced persistent threats (APTs), while anomaly-based systems often lack temporal intelligence required to predict escalation patterns. Modern cyber attacks frequently manifest as gradual behavioral drifts— subtle deviations in user or system behavior that precede significant compromise events. Detecting such drift early can prevent full-scale exploitation.

This research proposes an AI-Based Cyber Behavior Drift Detection and Attack Escalation Prediction System that integrates unsupervised anomaly detection with temporal deep learning to proactively identify early-stage cyber threats. The system monitors behavioral features including login frequency, command execution patterns, file access sequences, network traffic characteristics, and privilege usage trends. Isolation Forest is employed to detect anomalous deviations from established behavioral baselines, while a Temporal Convolutional Neural Network (Temporal CNN) models time-dependent patterns to predict potential escalation stages.

The proposed framework integrates real-time log ingestion, feature engineering, anomaly scoring, drift analysis, and predictive modeling within a scalable architecture suitable for enterprise environments. Experimental evaluation demonstrates improved early detection capability compared to traditional IDS approaches, reduced false positives through behavioral modeling, and accurate