Cloud Database Security Integrating Deep Learning And Machine Learning For Threat Detection And Prevention
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Cloud Database Security
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.
Cloud Database Security: Integrating Deep Learning and Machine Learning for Threat Detection and Prevention
Author: Rajendra Prasad Sola, Nihar Malali, Praveen Madugula
language: en
Publisher: Notion Press
Release Date: 2025-02-22
The topic of this book is the evolving landscape of cloud database security and its role in the threat of cyberattacks 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 realtime 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.
Utilizing AI in Network and Mobile Security for Threat Detection and Prevention
Artificial intelligence (AI) revolutionizes how organizations protect their digital information against cyber threats. Traditional security methods are often insufficient when faced with sophisticated attacks. AI-powered systems utilize machine learning, deep learning, and advanced analytics to detect patterns, identify anomalies, and predict potential threats in real time. By analyzing network traffic and mobile device behavior, AI can recognize and respond to malicious activity before it causes harm. This proactive approach enhances security protocols, reduces human error, and strengthens defenses against a wide range of cyberattacks, from malware to data breaches. Further research may reveal AI as an indispensable tool for securing networks and mobile environments, providing smarter, more adaptive solutions for threat detection and prevention. Utilizing AI in Network and Mobile Security for Threat Detection and Prevention explores the role of AI in enhancing cybersecurity measures. It examines AI techniques in anomaly and intrusion detection, machine learning for malware analysis and detection, predictive analytics to cybersecurity scenarios, and ethical considerations in AI. This book covers topics such as ethics and law, machine learning, and data science, and is a useful resource for computer engineers, data scientists, security professionals, academicians, and researchers.