Handbook of Big Data Privacy
Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 1. Big Data and Privacy : Challenges and Opportunities
- 2. AI and Security of Critical Infrastructure
- 3. Industrial Big Data Analytics: Challenges and Opportunities
- 4. A Privacy Protection Key Agreement Protocol Based on ECC for Smart Grid
- 5. Applications of Big Data Analytics and Machine Learning in the Internet of Things
- 6. A Comparison of State-of-the-art Machine Learning Models for OpCode-Based IoT Malware Detection
- 7. Artificial Intelligence and Security of Industrial Control Systems
- 8. Enhancing Network Security via Machine Learning: Opportunities and Challenges
- 9. Network Security and Privacy Evaluation Scheme for Cyber Physical Systems (CPS)
- 10. Anomaly Detection in Cyber-Physical Systems Using Machine Learning
- 11. Big Data Application for Security of Renewable Energy Resources
- 12. Big-Data and Cyber-Physical Systems in Healthcare: Challenges and Opportunities
- 13. Privacy Preserving Abnormality Detection: A Deep Learning Approach.-14. Privacy and Security in Smart and Precision Farming: A Bibliometric Analysis
- 15. A Survey on Application of Big Data in Fin Tech Banking Security and Privacy
- 16. A Hybrid Deep Generative Local Metric Learning Method For Intrusion Detection
- 17. Malware elimination impact on dynamic analysis: An experimental machine learning approach
- 18. RAT Hunter: Building Robust Models for Detecting Remote Access Trojans Based on Optimum Hybrid Features
- 19. Active Spectral Botnet Detection based on Eigenvalue Weighting