Machine Learning-Based Methodology and System Architecture for Anomaly Detection in Software Products

Anomaly detection has become a critical area of research in fields such as cybersecurity, system monitoring, and software engineering. Modern approaches utilize machine learning techniques to identify anomalies by learning patterns of expected behavior. This paper presents a technical characterization of anomalies in software systems and proposes a data-driven approach to anomaly detection. By using machine learning techniques, the proposed methodology aims to improve the adaptability, scalability, and accuracy of detection mechanisms in modern software products.