Handbook of Research on Machine and Deep Learning Applications for Cyber Security

Handbook of Research on Machine and Deep Learning Applications for Cyber Security
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Book Description
As the advancement of technology continues, cyber security continues to play a significant role in todays world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security.
The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.

Content

Chapter 1. Review on Intelligent Algorithms for Cyber Security
Chapter 2. A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms
Chapter 3. Review on Machine and Deep Learning Applications for Cyber Security
Chapter 4. Applications of Machine Learning in Cyber Security Domain
Chapter 5. Applications of Machine Learning in Cyber Security
Chapter 6. Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods
Chapter 7. Cyber Threats Detection and Mitigation Using Machine Learning
Chapter 8. Hybridization of Machine Learning Algorithm in Intrusion Detection System
Chapter 9. A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning
Chapter 10. Anomaly-Based Intrusion Detection: Adapting to Present and Forthcoming Communication Environments
Chapter 11. Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)
Chapter 12. A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder
Chapter 13. A 3D-Cellular Automata-Based Publicly-Verifiable Threshold Secret Sharing
Chapter 14. Big Data Analytics for Intrusion Detection: An Overview
Chapter 15. Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic
Chapter 16. A Secure Protocol for High-Dimensional Big Data Providing Data Privacy
Chapter 17. A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment
Chapter 18. Adoption of Machine Learning With Adaptive Approach for Securing CPS
Chapter 19. Variable Selection Method for Regression Models Using Computational Intelligence Techniques

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