Handbook of Research on Machine and Deep Learning Applications for Cyber Security (Advances in Information Security, Privacy, and Ethics)

As the advancement of technology continues, cyber security continues to play a significant role in today's 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. Cover Title Page Copyright Page Book Series List of Contributors Table of Contents Detailed Table of Contents Foreword Preface Acknowledgment 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 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 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 Compilation of References About the Contributors Index "This book explores the use of machine learning and deep learning applications in the areas of cyber security and cyber-attack handling mechanisms"-- Provided by publisher