Comprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML)
Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is a comprehensive look at state-of-the-art theory and practical guidelines pertaining to the subject, showcasing recent innovations, emerging trends, and concerns as well as applied challenges encountered, and solutions adopted in the fields of cybersecurity using analytics and machine learning. The text clearly explains theoretical aspects, framework, system architecture, analysis and design, implementation, validation, and tools and techniques of data science and machine learning to detect and prevent cyber threats.
Using AI and ML approaches, the book offers strategic defense mechanisms for addressing malware, cybercrime, and system vulnerabilities. It also provides tools and techniques that can be applied by professional analysts to safely analyze, debug, and disassemble any malicious software they encounter.
With contributions from qualified authors with significant experience in the field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection explores topics such as: - Cybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis- Applications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering- How AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats- Offensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint
Providing detailed coverage of a rapidly expanding field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning.
Table of Contents
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
