Building an AI-Driven Cybersecurity Model for Cloud-Based Anomaly Detection and Intrusion Response Management
Abstract
The increasing complexity of cloud infrastructures and the evolving sophistication of cyber threats have necessitated the integration of Artificial Intelligence (AI) into cybersecurity frameworks. This review explores the development of an AI-driven cybersecurity model designed to enhance anomaly detection and intrusion response management in cloud environments. Traditional rule-based systems often fail to adapt to emerging attack vectors or handle the massive volume of data generated in multi-tenant cloud architectures. By contrast, AI techniques—particularly machine learning, deep learning, and reinforcement learning—enable proactive detection of abnormal behaviors, adaptive defense mechanisms, and automated threat containment. The study examines hybrid models combining supervised and unsupervised algorithms for real-time anomaly identification, alongside the use of explainable AI for interpretability and trust in automated decision-making. Additionally, it reviews intrusion response automation frameworks integrating AI with security orchestration, automation, and response systems. Emphasis is placed on model scalability, data privacy, and adversarial robustness within cloud ecosystems. The paper concludes by highlighting future directions for integrating federated learning, edge intelligence, and self-healing architectures to achieve resilient and intelligent cybersecurity in cloud platforms. This review aims to bridge research and application by outlining a unified framework for AI-powered intrusion detection and response management systems.
Keywords
Artificial IntelligenceCloud SecurityAnomaly DetectionIntrusion ResponseMachine LearningCyber Threat Management.
References
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