Anomaly detection monitoring: A proactive approach with deep learning integration

Abstract

This study develops an enhanced anomaly detection framework that integrates state-of-the-art deep learning techniques. The system detects hate speech, malicious websites, vulnerabilities in open ports, and suspicious processes. It optimizes detection accuracy and reliability by leveraging tailored datasets, advanced model architectures, and refined preprocessing strategies. To ensure robustness, it employs comprehensive validation metrics, including the Polygon Area Metric (PAM). Empirical results show significant improvements in generalization and prediction accuracy over previous methodologies. The hate speech detection model achieved an accuracy of 88%, with precision of 75%, recall of 62%, F1 score of 62%, and ROC AUC of 92%. These results deliver a proactive monitoring solution suited for modern enterprise security challenges.