LogGuardQ leverages cognitive principles to improve exploration and stability in reinforcement learning scenarios. With a notable detection rate of 96.0% in simulated access logs, it demonstrates significant advantages over existing algorithms. The framework employs innovative strategies, including temperature decay and curiosity-based exploration, which contribute to its performance metrics.
In tests, LogGuardQ’s precision and recall rates highlight its effectiveness in identifying anomalies, while statistical analysis further confirms its superior performance. The implications of this research are substantial, suggesting a pathway for more adaptive learning systems capable of functioning effectively in cybersecurity, intrusion detection, and even broader decision-making contexts. As organizations face increasing challenges from cyber threats, tools like LogGuardQ could provide a robust solution for anomaly detection in real-time environments.
👉 Pročitaj original: arXiv AI Papers
