Self-Learning Threat Intelligence Systems Using Continual AI Models for Evolving Attack Patterns

Abstract

Polymorphic malware, sophisticated persistent threats, and adaptive attacks weaken static threat intelligence. Traditional machine learning-based cybersecurity systems use batch learning paradigms for periodic retraining, which delays reaction to new attack patterns and increases computing cost. Self-learning threat intelligence systems may absorb hostile data without retraining AI. We study incremental, transfer, and meta-learning methods for adaptive information retention and catastrophic forgetting prevention. Data stream processing and reinforcement learning for real-time model updates leverage live telemetry, network traffic analytics, and enterprise-scale infrastructure behavioral indicators.For context retention and attack defense, the proposed architecture employs memory-augmented neural networks and hierarchical feature extraction layers. Test dynamic cybersecurity scenarios for detection latency, classification accuracy, attacker resistance, and processing efficiency. Anomaly detection, trust-weighted model updating, and continuous learning model adversarial poisoning are described. Experimental simulations show that continuous learning-based threat intelligence systems detect zero-day vulnerabilities and polymorphic attack campaigns better than batch-learning. The project creates autonomous cybersecurity infrastructures for complex, fast-changing digital environments.