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Total Size:
13.3 MB
Info Hash:
8324317566FEDA04E649090821E8EA31AB21491B
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Added:
June 13, 2025, 11:37 a.m.
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(Last updated: June 17, 2025, 7 a.m.)
| File | Size |
|---|---|
| ['Bhambri P. Handbook of AI-Driven Threat Detection and Prevention...2025.pdf'] | 0 bytes |
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13.3 MB
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2025-06-13
| Uploaded by andryold1 | Size 13.3 MB | Health [ 13 /39 ] | Added 2025-06-13 |
NOTE
SOURCE: Bhambri P. Handbook of AI-Driven Threat Detection and Prevention...2025
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COVER
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MEDIAINFO
Textbook in PDF format Handbook of AI-Driven Threat Detection and Prevention: A Holistic Approach to Security explores AI-driven threat detection and prevention, and covers a wide array of topics such as machine learning algorithms, deep learning, natural language processing, and so on. The holistic view offers a deep understanding of the subject matter as it brings together insights and contributions from experts from around the world and various disciplines including computer science, cybersecurity, data science, and ethics. This comprehensive resource provides a well-rounded perspective on the topic and includes real-world applications of AI in threat detection and prevention emphasized through case studies and practical examples that showcase how AI technologies are currently being utilized to enhance security measures. Preface Understanding AI and Machine Learning in Security Data Collection and Preprocessing for Security Feature Engineering for Threat Detection Anomaly Detection with Artifcial Intelligence Signature-Based Security in Wireless Communication Behavioral Analysis for Threat Detection Network Security with Artifcial Intelligence Endpoint Security and Artifcial Intelligence in the Financial Sector Cloud Security and Artifcial Intelligence Adversarial Attacks on AI Security Systems: Investigating the Vulnerability of AI-Powered Security Solutions Ethical Considerations and Privacy in AI-Powered Security Artifcial Intelligence in Financial Fraud Detection Graph-Based Intelligent Cyber Threat Detection System Future Trends in Artifcial Intelligence Driven Security Enhancing Cybersecurity with Distributed Models and Sparse Mixture of Experts Anomaly Detection in SIEM Data: User Behavior Analysis with Artifcial Intelligence AI-Driven Security System for Biometric Surveillance AI-Powered Predictive Analysis for Proactive Cyber Defense AI-Driven Security System for Biometric Surveillance AI-Powered Predictive Analysis for Proactive Cyber Defense Deep Learning Techniques for Intrusion Detection in Critical Infrastructure Quantum Computing and AI Synergies: Strengthening Cybersecurity Resilience Integrating AI with Blockchain for Decentralized Security and Threat Prevention
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