AI-Driven Machine Learning Framework for Proactive Anomaly Detection in Mission-Critical Software Systems

Abstract

Mission-critical software systems operating in domains such as healthcare, aerospace, industrial automation, telecommunications, and cloud computing require highly reliable anomaly detection mechanisms to prevent service disruptions and catastrophic failures. Although recent advances in artificial intelligence (AI) and machine learning (ML) have significantly improved anomaly detection accuracy, existing approaches remain constrained by several limitations, including dependence on single-source monitoring data, limited adaptability to concept drift, inadequate support for real-time prediction, poor model interpretability, and insufficient integration with operational decision-making. Moreover, most current solutions focus on identifying anomalies after they have occurred rather than enabling proactive system resilience through early warning and intelligent risk assessment. This research proposes an AI-Driven Machine Learning Framework for Proactive Anomaly Detection in Mission-Critical Software Systems that addresses these shortcomings through a unified, multi-layered architecture. The proposed framework integrates heterogeneous operational data, including system logs, performance metrics, execution traces, and runtime events, to provide a comprehensive representation of system behaviour. It incorporates adaptive machine learning models capable of continuously learning from evolving software environments while mitigating concept drift through incremental model updates. To improve operational trust and decision support, the framework further combines explainable AI techniques with a risk-aware anomaly prioritization mechanism that assists system administrators in distinguishing critical anomalies from benign deviations. Unlike conventional anomaly detection systems that primarily perform retrospective fault identification, the proposed framework emphasizes proactive anomaly forecasting, intelligent alert prioritization, and early intervention to enhance software resilience. The study presents the conceptual architecture, methodological design, and evaluation strategy of the proposed framework using publicly available benchmark datasets and standard anomaly detection metrics, including Precision, Recall, F1-score, Area Under the ROC Curve (AUC), detection latency, false alarm rate, and computational efficiency. The anticipated contribution of this research is the development of an integrated AI-driven framework that bridges the gap between accurate anomaly detection and practical deployment in mission-critical software environments by combining adaptive learning, multimodal data fusion, explainable decision-making, and proactive operational intelligence. The proposed framework aims to improve the reliability, scalability, and resilience of next-generation software systems while providing actionable insights for real-time system management.