eBook For {EPUB} Applying Artificial Intelligence in Cybersecurity 1st Edition

About the Editors xvii

List of Contributors xxi

Preface xxv

Acknowledgment xxvii

Disclaimer xxix

Note for Readers xxxi

Introduction xxxiii

Part I Artificial Intelligence (AI) in Cybersecurity Analytics: Fundamental and Challenges 1

1 Analysis of Malicious Executables and Detection Techniques 3
Geetika Munjal and Tushar Puri

1.1 Introduction 3

1.2 Malicious Code Classification System 5

1.3 Literature Review 5

1.4 Malware Behavior Analysis 8

1.5 Conventional Detection Systems 11

1.6 Classifying Executables by Payload Function 12

1.7 Result and Discussion 13

1.8 Conclusion 15

2 Detection and Analysis of Botnet Attacks Using Machine Learning Techniques 19
Supriya Raheja

2.1 Introduction 19

2.2 Literature Review 20

2.3 Botnet Architecture 21

2.4 Methodology Adopted 24

2.5 Experimental Setup 27

2.6 Results and Discussions 28

2.7 Conclusion and Future Work 30

3 Artificial Intelligence Perspective on Digital Forensics 33
Bhawna and Shilpa Mahajan

3.1 Introduction 33

3.2 Literature Survey 34

3.3 Phases of Digital Forensics 35

3.4 Demystifying Artificial Intelligence in the DigitalWorld 36

3.5 Application of Machine Learning in Digital Forensics Investigations 39

3.6 Implementation of Artificial Intelligence in Forensics 40

3.7 Pattern Recognition Using Artificial Intelligence 40

3.8 Applications of AI in Criminal Investigations 42

3.9 Conclusion 43

4 Review on Machine Learning-based Traffic Rules Contravention Detection System 45
Jahnavi and Urvashi

4.1 Introduction 45

4.2 Technologies Involved in Smart Traffic Monitoring 47

4.3 Literature Review 50

4.4 Comparison of Results 59

4.5 Conclusion and Future Scope 59

5 Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies 63
Vishwas Pitre, Ashish Joshi, Satya Saladi, and Suman Das

5.1 Introduction 63

5.2 Literature Review 66

5.3 Proposed Methodology 67

5.4 Results 75

5.5 Conclusion and Future Scope ofWork 84

Part II Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data 87

6 Malware Analysis Techniques in Android-Based Smartphone Applications 89
Geetika Munjal, Avi Chakravarti, and Utkarsh Sharma

6.1 Introduction 89

6.2 Malware Analysis Techniques 93

6.3 Hybrid Analysis 102

6.4 Result 102

6.5 Conclusion 103

7 Cyber Threat Detection and Mitigation Using Artificial Intelligence — A Cyber-physical Perspective 107
Dalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Anand Deshpande, Dhanashree Kulkarni, Andrey Terziev, Maria A. de Jesus, and Edwiges G.H. Grata

7.1 Introduction 107

7.2 Types of Cyber Threats 109

7.3 Cyber Threat Intelligence (CTI) 116

7.4 Materials and Methods 119

7.5 Cyber-Physical Systems Relying on AI (CPS-AI) 121

7.6 Experimental Analysis 126

7.7 Conclusion 129

8 Performance Analysis of Intrusion Detection System Using ML Techniques 135
Paridhi Pasrija, Utkarsh Singh, and Mehak Khurana

8.1 Introduction 135

8.2 Literature Survey 136

8.3 ML Techniques 137

8.4 Overview of Dataset 140

8.5 Proposed Approach 142

8.6 Simulation Results 143

8.7 Conclusion and Future Work 148

9 Spectral Pattern Learning Approach-based Student Sentiment Analysis Using Dense-net Multi Perception Neural Network in E-learning Environment 151
Laishram Kirtibas Singh and R. Renuga Devi

9.1 Introduction 151

9.2 RelatedWork 152

9.3 Proposed Implementation 153

9.4 Result and Discussion 159

9.5 Conclusion 163

10 Big Data and Deep Learning-based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network 165
Chingakham Nirma Devi and R. Renuga Devi

10.1 Introduction 165

10.2 RelatedWork 166

10.3 Materials and Method 168

10.4 Result and Discussion 173

10.5 Conclusion 176

Part III Applied Artificial Intelligence Approaches in Emerging Cybersecurity Domains 179

11 Enhancing Security in Cloud Computing Using Artificial Intelligence (AI) 181
Dalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Nikolaos Andreopoulos, Andrey Terziev, Anand Deshpande, Dhanashree Kulkarni, and Edwiges G.H. Grata

11.1 Introduction 181

11.2 Background 184

11.3 Identification Function (IF) 185

11.4 Protection Function (PF) 191

11.5 Detection Function (DF) 196

11.6 Response Function (RF) 200

11.7 Recovery Function (RcF) 205

11.8 Analysis, Discussion and Research Gaps 205

11.9 Conclusion 209

12 Utilization of Deep Learning Models for Safe Human-Friendly Computing in Cloud, Fog, and Mobile Edge Networks 221
Diego M.R. Tudesco, Anand Deshpande, Asif A. Laghari, Abdullah A. Khan, Ricardo T. Lopes, R. Jenice Aroma, Kumudha Raimond, Lin Teng, and Asiya Khan

12.1 Introduction 221

12.2 Human-Centered Computing (HCC) 223

12.3 Improving Cybersecurity Through Deep Learning (DL) Models: AI-HCC Systems 229

12.5 Discussion 238

12.6 Conclusion 239

13 Artificial Intelligence for Threat Anomaly Detection Using Graph Databases — A Semantic Outlook 249
Edwiges G.H. Grata, Anand Deshpande, Ricardo T. Lopes, Asif A. Laghari, Abdullah A. Khan, R. Jenice Aroma, Kumudha Raimond, Shoulin Yin, and Awais Khan Jumani

13.1 Introduction 249

13.2 KGs in Cybersecurity 252

13.3 CSKG Construction Methodologies 254

13.3.1 CSKG Building Flow 255

13.3.2 CS Ontology 255

13.3.3 CS Entities Extraction 256

13.3.4 Relations Extraction of CS Entities 257

13.4 Datasets 258

13.5 Application Scenarios 259

13.5.1 CSA and Security Assessment 259

13.5.2 CTs’ Discovery 260

13.5.3 Attack Probing 261

13.5.4 Clever Security Operation 264

13.5.5 Smart Decision-Making 265

13.5.6 Vulnerability Prediction and Supervision 266

13.5.7 Malware Acknowledgment and Analysis 267

13.5.8 Physical System Connection 267

13.5.9 Supplementary Reasoning Tasks 268

13.6 Discussion and Future Trends on CSKG 269

13.7 Conclusion 271

14 Security in Blockchain-Based Smart Cyber-Physical Applications Relying on Wireless Sensor and Actuators Networks 279
Maria A. de Jesus, Asif A. Laghari, Abdullah A. Khan, Awais Khan Jumani, Mohammad Shabaz, Anand Deshpande, R. Jenice Aroma, Kumudha Raimond, and Asiya Khan

14.1 Introduction 279

14.2 Methodology 282

14.3 GIBCS: An Overview 292

14.4 Blockchain Layer 294

14.5 Trust Management 296

14.6 Blockchain for Secure Monitoring Back-End 298

14.7 Blockchain-Enabled Cybersecurity: Discussion and Future Directions 300

14.8 Conclusions 301

15 Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data 311
Keshav Kaushik

15.1 Introduction to the IoT Security 311

15.2 Role of Deep Learning in IoT Security 316

15.3 Deep Learning Architecture for IoT Security 319

15.4 Future Scope of Deep Learning in IoT Security 322

15.5 Conclusion 323

References 323

Index 327