Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques

Anomaly detection is a critical aspect of ensuring the security and reliability of various systems in diverse domains, including cybersecurity, finance, and industrial processes. Traditional black-box anomaly detection methods often lack interpretability, making it challenging for users to understand the reasoning behind the detection of anomalies. In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule-based system that generates interpretable anomaly detection rules with machine learning components that leverage complex pattern recognition and classification capabilities. We evaluate the proposed approach on a diverse set of real-world datasets, demonstrating its effectiveness in identifying anomalies while providing transparent explanations of the detection process. Through comprehensive experimentation and comparative analysis with existing state-of-the-art methods, we showcase the superior interpretability and performance of our hybrid approach. Our findings highlight the significance of interpretability in anomaly detection systems and underscore the potential of the proposed approach for enhancing transparency and trust in critical decision-making processes. This research contributes to the advancement of interpretable anomaly detection techniques and opens avenues for future research in the domain of transparent and reliable anomaly detection systems.