Autonomous AI-Based Cloud Security Monitoring and Attack Prediction System Using Deep Neural Networks

Abstract

Autonomous AI-Based Cloud Security Monitoring and Attack Prediction System Using Deep Neural Networks presents a comprehensive approach to modern cloud cybersecurity by combining cloud monitoring, artificial intelligence, deep learning, anomaly detection, attack classification, threat prediction, risk assessment, and automated security response.As organizations increasingly depend on cloud computing, the volume and complexity of security events continue to grow. Traditional security mechanisms based only on predefined rules and signatures may not be sufficient to identify continuously evolving attack behaviours. This book explores how Deep Neural Networks (DNNs) can be integrated with cloud security monitoring to analyse large volumes of security data and identify suspicious behavioural patterns.The book provides a systematic discussion of cloud security environments, common threats, security monitoring architectures, dataset preparation, data preprocessing, feature engineering, DNN model development, anomaly detection, attack classification, and attack prediction. It also explains how security events can be correlated over time to identify multi-stage attack patterns.A major focus of the book is the integration of AI-based security intelligence with risk assessment and autonomous response mechanisms. The system architecture demonstrates how security events can be collected from cloud resources, transformed into meaningful features, analysed by deep-learning models, classified according to potential attack categories, and converted into actionable security alerts.The book also covers cloud deployment, APIs, databases, security dashboards, authentication, encryption, logging, model monitoring, scalability, reliability, and controlled automated response. Detailed testing and evaluation methodologies are presented using metrics such as accuracy, precision, recall, F1-score, confusion matrix, false-positive rate, false-negative rate, detection latency, prediction latency, throughput, and resource utilization.Beyond detection, the book discusses predictive and adaptive cybersecurity concepts, including model drift, continuous learning, explainable AI, threat intelligence, zero-day attack detection, federated learning, graph-based security analysis, transformer-based models, multi-cloud monitoring, container security, and autonomous threat hunting.This book is intended for students, researchers, educators, cybersecurity professionals, cloud engineers, software developers, and readers interested in the intersection of artificial intelligence, deep learning, cloud computing, and cybersecurity.Whether used as an academic reference, project-development guide, or introduction to intelligent cloud security, this book provides a structured foundation for understanding how AI can support the transition from traditional reactive security monitoring toward predictive, adaptive, and increasingly autonomous cloud security operations.