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Total Size:
13.3 MB
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8324317566FEDA04E649090821E8EA31AB21491B
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June 13, 2025, 11:37 a.m.
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(Last updated: June 17, 2025, 7 a.m.)
| File | Size |
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| ['Bhambri P. Handbook of AI-Driven Threat Detection and Prevention...2025.pdf'] | 0 bytes |
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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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