Beyond Traditional Methods: A Novel Approach to Anomaly Detection and Classification Using AI Techniques

Balaji Dhamodharan

Abstract


Anomalies in complex systems pose significant challenges to operational efficiency, safety, and security. This research introduces a pioneering approach leveraging artificial intelligence (AI) techniques to address this issue. Our methodology integrates advanced machine learning algorithms with domain-specific knowledge to develop a robust framework for anomaly detection and classification. Central to our approach is the utilization of deep learning architectures and anomaly detection models to capture complex patterns in high-dimensional data. Through extensive experimentation across diverse domains, including industrial control systems, cybersecurity, and healthcare, our approach consistently outperformed baseline methods. Quantitative analysis reveals compelling results, with our framework achieving an average precision of 0.92, recall of 0.89, F1-score of 0.90, and AUC-ROC of 0.95 across all tested datasets. Comparative analysis demonstrates significant improvements over traditional methods, highlighting the superior accuracy and robustness of our approach in detecting anomalies. Moreover, our framework demonstrated scalability and adaptability across different data types and system architectures, reaffirming its efficacy in enhancing anomaly detection in real-world applications. Our research presents a groundbreaking solution for addressing anomalies in complex systems using AI, offering higher accuracy, scalability, and adaptability compared to existing methods. This framework holds promise for improving system resilience and security across diverse domains.

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References


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