Cloud Database Security
Integrating Deep Learning and Machine Learning for Threat Detection and Prevention
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The topic of this book is the evolving landscape of cloud database security and its role in the threat of cyber-attacks by artificial intelligence (AI). It begins with the introduction of basic cloud computing concepts, points out the significant security problems, and describes data protection as important. The authors delve into deep learning (DL) and machine learning (ML) techniques for real-time threat detection, anomaly identification, and intrusion prevention. The book covers the use of AI for security mechanisms, predictive analytics, and automated threat intelligence sharing. It also d...
The topic of this book is the evolving landscape of cloud database security and its role in the threat of cyber-attacks by artificial intelligence (AI). It begins with the introduction of basic cloud computing concepts, points out the significant security problems, and describes data protection as important. The authors delve into deep learning (DL) and machine learning (ML) techniques for real-time threat detection, anomaly identification, and intrusion prevention. The book covers the use of AI for security mechanisms, predictive analytics, and automated threat intelligence sharing. It also discusses new developments, such as federated learning, blockchain security, and homomorphic encryption. In addition, the text deals with the risks of quantum computing, regulation compliance, and rising threats. The book is a standalone cybersecurity reference for students, professionals, and researchers based on acknowledged theoretical ideas and practical applications. Cloud security should include AI and ML to improve integrity and resilience against smart threats.
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