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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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