INTELLIGENT ANOMALY DETECTION IN DISTRIBUTED SYSTEMS VIA DEEP LEARNING

This paper presents a novel framework for intelligent anomaly detection in distributed systems, leveraging deep learning techniques to enhance the identification of anomalies in real-time. As organizations increasingly depend on distributed architectures—such as cloud computing, microservices, and peer-to-peer networks—ensuring the reliability and security of these systems becomes crucial. Anomalies, which signify deviations from expected behavior, can indicate serious issues ranging from system malfunctions to security breaches. Traditional anomaly detection methods often struggle in distributed environments due to their reliance on predefined thresholds and assumptions about data distributions, leading to high rates of false positives and negatives. This study explores the potential of deep learning models, including Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders, to address these challenges. The proposed framework encompasses data collection, preprocessing, model selection, training, evaluation, and deployment, facilitating systematic anomaly detection while enabling continuous learning. The results indicate that deep learning models significantly outperform traditional methods, demonstrating their ability to capture complex patterns in high-dimensional data. Furthermore, the findings suggest that advancements in deep learning and hybrid approaches could further enhance anomaly detection capabilities across various domains, including finance, healthcare, and cybersecurity. This research contributes to the field by providing a comprehensive methodology for intelligent anomaly detection tailored to the unique challenges of distributed systems, paving the way for more resilient and secure computing environments.