Hybrid Deep Learning and Reinforcement Learning Strategies for Advanced Anomaly Detection in Complex Systems

Abstract

Complex systems in modern industrial, cybersecurity, and infrastructure domains generate massive volumes of heterogeneous data, presenting significant challenges for traditional anomaly detection approaches. This paper proposes a novel hybrid framework that integrates Deep Learning (DL) and Reinforcement Learning (RL) strategies to address the limitations of existing methods in detecting sophisticated anomalies within complex systems. The proposed Hybrid Deep Learning-Reinforcement Learning (HDL-RL) framework combines the representational power of deep neural networks for feature extraction with the adaptive decision-making capabilities of reinforcement learning agents. Our approach employs residual convolutional neural networks and recurrent architectures for hierarchical feature learning, while policy-based reinforcement learning algorithms enable dynamic threshold adaptation and detection strategy optimization. The framework addresses key challenges including concept drift, imbalanced datasets, temporal dependencies, and the need for interpretable decisions in critical system monitoring. Experimental evaluation across multiple domains including network intrusion detection, industrial process monitoring, and financial fraud detection demonstrates significant performance improvements over state-of-the-art approaches. The HDL-RL framework achieves average precision improvements of 18.2% and recall enhancements of 15.7% while maintaining computational efficiency suitable for real-time deployment. The adaptive nature of the reinforcement learning component enables continuous improvement in detection accuracy as the system encounters new anomaly patterns, making it particularly suitable for evolving threat landscapes and dynamic operational environments.