Intelligent Approaches to Cyber Security 1st edition by Narendra Shekokar,Hari Vasudevan,Surya Durbha,Antonis Michalas,Tatwadarshi Nagarhalli 9781000961652 1000961656

Section I Introduction to Machine Learning in Cyber Security

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

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

2.4.2 Heart Disease

2.4.3 Breast Cancer

2.4.4 Diabetes

2.4.5 Voice Disorder

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

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

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

4.6 Experimentation and Results

4.7 Future Applications

4.8 Conclusion

References

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

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

6.2.2 Deep Learning Approach for Financial Fraud Detection

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

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

7.4.4 Number of Features

7.5 Conclusion

References

Section III Defending Against Cyber Attack Using Deep Learning

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

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

9.5.2 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

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

10.3.4 Cyber-Threat Environment

10.3.5 Types of Cyber Threats

10.4 Using Technologies to Mitigate Cyber Threats

10.4.1 Comparison of Common Techniques

10.4.2 Artificial Intelligence

10.4.3 Machine Learning

10.4.4 Deep Learning

10.4.5 Blockchain

10.5 Cyberinfrastructure

10.6 Conclusion

10.7 Future Scope

References

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