Handbook of Research on Machine and Deep Learning Applications for Cyber Security 1st edition by Padmavathi Ganapathi 1522596143 9781522596141

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ISBN-10 :  1522596143 

ISBN-13 :  9781522596141

Author :  Padmavathi Ganapathi 

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.

Handbook of Research on Machine & Deep Learning Applications for Cyber Security 1st Table of contents:

Chapter 1: Review on Intelligent Algorithms for Cyber Security
ABSTRACT
INTRODUCTION
STUDY ON NIC ALGORITHMS IN CYBER SECURITY
STUDY ON MACHINE LEARNING IN CYBER SECURITY
STUDY ON DEEP LEARNING IN CYBER SECURITY
INFERENCES
RESEARCH SUMMARY
CONCLUSION AND FUTURE WORKS
REFERENCES
KEY TERMS AND DEFINITIONS
Chapter 2: A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms
ABSTRACT
INTRODUCTION
CYBER SECURITY SOLUTIONS USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN LITERATURE:
HTTP CSIC 2010 DATASET
CONCLUSION
REFERENCES
Chapter 3: Review on Machine and Deep Learning Applications for Cyber Security
ABSTRACT
INTRODUCTION
COMMON CYBERSECURITY THREATS AND ATTACKS
MACHINE LEARNING FOR CYBER SECURITY
LITERATURE REVIEW OF USING MACHINE LEARNING IN CYBER SECURITY
APPLICATION OF MACHINE LEARNING
DEEP LEARNING FOR CYBER SECURITY
LITERATURE SURVEY OF USING DEEP LEARNING IN CYBER SECURITY
CYBERSECURITY SOLUTION -INDUSTRIAL APPROACH WITH ML AND DL
PROBLEMS TO BE ADDRESSED
CONCLUSION AND FUTURE WORK
REFERENCES
Chapter 4: Applications of Machine Learning in Cyber Security Domain
ABSTRACT
INTRODUCTION
CYBER SECURITY AND CYBER CRIME
MACHINE LEARNING IN CYBER SECURITY
RELATED RESEARCH WORK ON MACHINE LEARNING IN CYBER SECURITY
APPLICATIONS OF MACHINE LEARNING ALGORITHMS TO CYBER SECURITY
APPLICATIONS OF MACHINE LEARNING IN CYBER SECURITY DOMAIN
CHALLENGES IN MACHINE LEARNING
FUTURE TRENDS
REFERENCES
Chapter 5: Applications of Machine Learning in Cyber Security
ABSTRACT
INTRODUCTION
WHAT IS MACHINE LEARNING?
APPROACHES TO CLASSIFY MACHINE LEARNING ALGORITHMS
SHALLOW LEARNING
DEEP LEARNING
APPLICATIONS OF MACHINE LAERNING
MACHINE LEARNING TASKS AND CYBER-SECURITY
PROPOSED CYBER-SECURITY TASKS AND MACHINE LEARNING
FRONTIERS OF CYBER SECURITY
CONCLUSION
REFERENCES
Chapter 6: Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods
ABSTRACT
INTRODUCTION
KNOW-HOW: UNDERSTANDING ABOUT MALWARE IN ITS DEPTH AND BREADTH
MALWARE ANALYSIS AND DETECTION TECHNIQUES
A COMPARISON OF THE REVIEWED MALWARE CLASSIFICATION ALGORITHMS
DEEP LEARNING (DL)
MACHINE LEARNING(ML) VS DEEP LEARNING(DL)
CONCLUSION
REFERENCES
Chapter 7: Cyber Threats Detection and Mitigation Using Machine Learning
ABSTRACT
INTRODUCTION
CYBER THREATS
CYBER ATTACKS
MACHINE LEARNING
ANALYSIS OF MACHINE LEARNING APPROACHES IN THREATS DETECTION
CONCLUSION
REFERENCES
Chapter 8: Hybridization of Machine Learning Algorithm in Intrusion Detection System
ABSTRACT
INTRODUCTION
MACHINE LEARNING IN INTRUSION DETECTION SYSTEM
SWARM INTELLIGENCE
RELATED STUDIES
DATASET DESCRIPTION AND VALIDATION
DATA PRE-PROCESSING
EXPERIMENTAL SETUP
VALIDATION METHOD
METHODOLOGY
ANALYSIS ON CVM-CSA APPROACH
CONCLUSION
REFERENCES
Chapter 9: A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning
ABSTRACT
INTRODUCTION
MOBILE APPS
MALWARE APPS: BACKGROUND STUDY
PATTERN RECOGNITION
APPLICATION PROGRAMMING INTERFACE (API) CALLS
DEEP LEARNING
BUSTER SANDBOX ANALYZER
SCOPE OF BUSTER SANDBOX ANALYZER
TECHNIQUES FOR MALWARE DETECTION
PROPOSED SYSTEM
CONCLUSION
REFERENCES
Chapter 10: Anomaly-Based Intrusion Detection
ABSTRACT
INTRODUCTION
RELATED RESEARCH TOPICS
CHARACTERIZATIONS OF ANOMALIES
KNOWLEDGE ACQUISITION AND MODELING
DISTANCES AND SIMILARITY MEASURES
NON-STATIONARITY
EVALUATION AND METRICS
DIFFICULTIES AND CHALLENGES
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 11: Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)
ABSTRACT
INTRODUCTION
BACKGROUND
METHODOLOGY
PROPOSED TECHNIQUE
SIMULATION ENVIRONMENT
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
ADDITIONAL READING
KEY TERMS AND DEFINITIONS
Chapter 12: A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder
ABSTRACT
INTRODUCTION
IMAGE ENHANCEMENT WITH DEEP AUTOENCODER AND UNDECIMATED WAVELET TRANSFORM
FUTURE RESEARCH DIRECTIONS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter 13: A 3D-Cellular Automata-Based Publicly-Verifiable Threshold Secret Sharing
ABSTRACT
INTRODUCTION
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
ADDITIONAL READING
KEY TERMS AND DEFINITIONS
Chapter 14: Big Data Analytics for Intrusion Detection
ABSTRACT
INTRODUCTION
BIG DATA AND MACHINE LEARNING
CYBERSECURITY CHALLENGES
RECENT STUDIES
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
KEY TERMS AND DEFINITIONS
ENDNOTES
Chapter 15: Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic
ABSTRACT
INTRODUCTION
CYBER SECURITY
ANOMALY DETECTION IN CYBER SECURITY
WHY ANOMALY DETECTION FOR CYBER SECURITY IS IMPORTANT?
BIG DATA ANALYTICS
BIG DATA AND MACHINE LEARNING IN CYBER SECURITY
BIG DATA AND DEEP LEARNING IN CYBER SECURITY
ANOMALY DETECTION DATASETS AND ISSUES
PERFORMANCE METRICS
CONCLUSION AND FUTURE SCOPE
REFERENCES
Chapter 16: A Secure Protocol for High-Dimensional Big Data Providing Data Privacy
ABSTRACT
INTRODUCTION
DATA ANONYMIZATION
BACKGROUND
PROPOSED SYSTEM
UNION: A SECURE PROTOCOL FOR HIGH DIMENSIONAL
EXPERIMENTAL RESULTS
CONCLUSION AND FURTHER WORK
REFERENCES
Chapter 17: A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment
ABSTRACT
INTRODUCTION
ATTACKS ON CLOUD
MACHINE LEARNING
CONCLUSION
REFERENCES
Chapter 18: Adoption of Machine Learning With Adaptive Approach for Securing CPS
ABSTRACT
INTRODUCTION
IMPORTANCE OF SECURITY IN CPS
ATTACKS ON CPS
MACHINE LEARNING AND DEEP LEARNING
DEEP LEARNING
MACHINE LEARNING ON CYBER ATTACK
MACHINE LEARNING SECURITY FOR CPS
CONCLUSION
REFERENCES
Chapter 19: Variable Selection Method for Regression Models Using Computational Intelligence Techniques
ABSTRACT
INTRODUCTION
METHODS AND MATERIALS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
KEY TERMS AND DEFINITIONS

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