Applying artificial intelligence in cyber security analytics and cyber threat detection / edited by Shilpa Mahajan, Mehak Khurana, Vania Vieira Estrela.
Contributor(s): Mahajan, Shilpa [editor.] | Khurana, Mehak [editor.] | Estrela, Vania Vieira [editor.]
Language: English Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., [2024]Description: 1 online resource (xxxiv, 333 pages) : illustrations (some color)Content type: text Media type: computer Carrier type: online resourceISBN: 1394196466; 9781394196456; 1394196458; 9781394196470; 1394196474; 9781394196463Subject(s): Computer security -- Technological innovations | Artificial intelligence | Computer networks -- Security measures | Computer crimes -- PreventionGenre/Form: Electronic books.DDC classification: 006.3 LOC classification: QA76.9.A25 | A674 2024Online resources: Full text is available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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COLLEGE LIBRARY | COLLEGE LIBRARY | 006.3 (Browse shelf) | Available |
Includes bibliographical references and index.
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
"Today, it's impossible to deploy effective cybersecurity technology without relying heavily on machine learning. With machine learning, cybersecurity systems can be analyzed using patterns and learn from them to help prevent similar attacks and respond to changing behavior. It can help cybersecurity teams to be more proactive in preventing threats and responding to active attacks in real time. In short, machine learning can make cybersecurity simpler, more proactive, less expensive, and far more effective. AI cybersecurity, with the support of machine learning, is set to be a powerful tool in the looming future. As with other industries, human interaction has long been essential and irreplaceable in security. While cybersecurity currently relies heavily on human input, we are gradually seeing technology become better at specific tasks than we are"-- Provided by publisher.
About the Author
Shilpa Mahajan, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.
Mehak Khurana, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.
Vania Vieira Estrela, PhD, is a Professor with the Telecommunications Department of the Fluminense Federal University, Brazil.
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