Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Section I: Introduction to Machine Learning in Cyber Security
Chapter 1: Introduction and Importance of Machine Learning Techniques in Cyber Security
1.1 Introduction
1.2 Importance of ML Techniques in Cyber Security
1.2.1 Common Vulnerabilities
1.2.2 Assets That Need to Be Protected
1.2.3 Role of Machine Learning in Cyber Security
1.3 Stages of a Cyber-Attack
1.4 Conclusion
References
Chapter 2: Review of Machine Learning Approaches in the Field of Healthcare
2.1 Introduction
2.2 Machine Learning Algorithms
2.3 Machine Learning Models
2.4 Disease Detection Using ML
2.4.1 Thyroid Disease
2.4.1.1 Methodology
2.4.2 Heart Disease
2.4.2.1 Methodology
2.4.3 Breast Cancer
2.4.3.1 Methodology
2.4.4 Diabetes
2.4.4.1 Methodology
2.4.5 Voice Disorder
2.4.5.1 Methodology
2.5 Security in Machine Learning/Deep Learning (ML/DL) for Healthcare
2.6 Security of ML
2.6.1 Security Threats
2.7 Conclusion and Future Scope
References
Chapter 3: Scope of Machine Learning and Blockchain in Cyber Security
3.1 Introduction
3.2 Machine Learning for Cyber Security
3.2.1 Threat Model for ML
3.3 Blockchain for Cyber Security
3.3.1 Threat Model for Blockchain
3.4 Proposed Approach
3.5 Conclusion and Future Work
References
Section II: Defending Against Cyber Attack Using Machine Learning
Chapter 4: Detection of Spear Phishing Using Natural Language Processing
4.1 Introduction
4.2 Literature Review
4.3 Dataset
4.4 Data Preprocessing
4.5 Textual Anomaly Detection System
4.5.1 Style Detection
4.5.1.1 Architecture of AWD-LSTM
4.5.1.2 Training of the Proposed Model
4.6 Experimentation and Results
4.7 Future Applications
4.8 Conclusion
References
Chapter 5: A Study of Recent Techniques to Detect Zero-Day Phishing Attacks
5.1 Introduction
5.2 Phishing Detection Approaches
5.2.1 Education-Based Detection
5.2.2 List-Based Detection
5.2.3 Heuristic-Based Detection
5.2.4 Content-Based Detection
5.2.5 Hybrid Detection Technique
5.3 Phishing Detection Using Machine Learning
5.4 Anti-Phishing Solutions Using Neural Networks/Deep Learning
5.4.1 Solutions Using Neural Networks
5.4.2 Solutions Using Deep Learning
5.5 Machine Learning as a Warhead
5.6 Comparative Analysis of the Techniques
5.7 Conclusion
References
Chapter 6: Analysis of Intelligent Techniques for Financial Fraud Detection
6.1 Introduction
6.2 Financial Fraud Detection Approaches
6.2.1 Machine Learning Approach for Financial Fraud Detection
6.2.1.1 Challenges in the Machine Learning Approach
6.2.1.2 Limitations of the Machine Learning Approach
6.2.2 Deep Learning Approach for Financial Fraud Detection
6.2.2.1 Challenges in the Deep Learning Approach
6.2.2.2 Limitations of the Deep Learning Approach
6.3 Literature Survey
6.3.1 Literature Survey for Machine Learning Approach
6.3.2 Literature Survey for Deep Learning Approach
6.4 Proposed Model Architecture
6.5 Confusion Matrix
6.6 Comparative Analysis of Decision Tree, SVM and Random Forest Algorithms
6.7 Conclusion
6.8 Future Scope
References
Further Reading
Chapter 7: Evaluation of Learning Techniques for Intrusion Detection Systems
7.1 Introduction
7.2 Review of Literature
7.3 Dataset
7.4 Analysis of Machine Learning Techniques
7.4.1 Input Data
7.4.2 Number of Classes
7.4.3 Number of Training Instances for Each Class
7.4.3.1 Oversampling
7.4.3.2 Undersampling
7.4.4 Number of Features
7.5 Conclusion
References
Section III: Defending Against Cyber Attack Using Deep Learning
Chapter 8: Deep Neural Networks for Cybersecurity
8.1 Introduction
8.2 Pitfalls in Traditional Cyber Security
8.2.1 Denial-of-Service (DoS) Attacks
8.2.2 Social Engineering
8.2.3 Phishing
8.2.4 Malware
8.2.5 Data Breach
8.3 Proposed Deep Learning Architectures and Methodologies
8.3.1 Convolutional Neural Networks
8.3.2 Recurrent Neural Networks
8.3.3 Generative Adversarial Networks
8.4 Deep Learning Applications in Cyber Security
8.4.1 Intrusion Detection Systems (IDS/IPS) with Network Traffic Analytics
8.4.2 Social Engineering Detection
8.4.3 Malware Detection
8.5 Drawbacks and Future Scope
8.6 Conclusion
References
Chapter 9: Deep Learning in Malware Identification and Classification
9.1 Introduction
9.2 Malware and Its Variants
9.3 Current Malware Statistics
9.4 Malware Detection
9.4.1 Anomaly-Based Detection
9.4.2 Signature-Based Detection
9.5 Machine Learning in Malware Detection
9.5.1 Neural Networks for Malware Detection
9.5.1.1 Connections and Weights
9.5.1.2 Propagation Function/Activation Function
9.5.1.3 Learning Process
9.5.2 Detection Using Combined Features
9.6 Malware Visualization and Classification Using Deep Learning
9.6.1 Collection of Malware Samples and Preprocessing
9.6.2 Visualization of Malware as an Image
9.6.3 Training a Neural Network Model for Classification
9.6.4 Testing/Validation of a Model
9.7 Summary
References
Section IV: Defending Against Cyber Attack Using Advance Technology
Chapter 10: Cyber Threat Mitigation Using Machine Learning, Deep Learning, Artificial Intelligence, and Blockchain
10.1 Introduction
10.2 Literature Survey
10.3 Cyber Threats
10.3.1 What Is a Cyber Threat?
10.3.2 Cyber-Threat Actors
10.3.3 Sources of Cyber Threat
10.3.3.1 Terrorists
10.3.3.2 Insiders
10.3.3.3 Nations
10.3.4 Cyber-Threat Environment
10.3.5 Types of Cyber Threats
10.3.5.1 Botnets
10.3.5.2 Denial of Service
10.3.5.3 Man-in-the-Middle
10.3.5.4 Password Cracking
10.3.5.5 Ransomware
10.4 Using Technologies to Mitigate Cyber Threats
10.4.1 Comparison of Common Techniques
10.4.2 Artificial Intelligence
10.4.2.1 Introduction to AI in Cyber Security
10.4.2.2 Use of AI in Cyber Security
10.4.2.2.1 Exposing Cyber Threats
10.4.2.2.2 Prediction of Breaching
10.4.2.2.3 Response to Incidences
10.4.3 Machine Learning
10.4.3.1 Use of Machine Learning in Reducing Cyber Threats
10.4.3.1.1 Automated Security
10.4.3.1.2 Advanced Antivirus Programs
10.4.3.1.3 Bane or Boon?
10.4.4 Deep Learning
10.4.4.1 Deep learning for Detection
10.4.4.1.1 Email Surveillance
10.4.4.1.2 Network Traffic Monitoring
10.4.5 Blockchain
10.4.5.1 What Is Blockchain?
10.4.5.2 Nature of Blockchain
10.4.5.3 Use of Blockchain in Data Integrity
10.4.5.4 Use Cases of Blockchain in Cyber Security
10.4.5.4.1 Decentralized Storage
10.4.5.4.2 Securing DNS
10.5 Cyberinfrastructure
10.6 Conclusion
10.7 Future Scope
References
Chapter 11: Quantum-Safe Cryptography
11.1 Introduction
11.2 Current State of Cryptosystems
11.2.1 Security Issues with Current Cryptosystems
11.3 Current State of Post-Quantum Cryptography (PQC)
11.4 Challenges in Post-Quantum Cryptography (PQC)
11.5 Approaches for Post-Quantum Cryptography (PQC) Migration
11.5.1 Hybrid Scheme
11.5.2 Protective Measures for Pre-Quantum Cryptography
References
Index