Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection

Inbunden, Engelska, 2024

1 256 kr

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Beskrivning

APPLYING ARTIFICIAL INTELLIGENCE IN CYBERSECURITY ANALYTICS AND CYBER THREAT DETECTION 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 analysisApplications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineeringHow AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threatsOffensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpointProviding 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.

Produktinformation

Utgivningsdatum:2024-03-18
Mått:152 x 229 x 21 mm
Vikt:776 g
Format:Inbunden
Språk:Engelska
Antal sidor:368
Förlag:John Wiley & Sons Inc
ISBN:9781394196449

Utforska kategorier

Nätverk och kommunikation inom Data och IT

Mer om författaren

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.

Innehållsförteckning

About the Editors xviiList of Contributors xxiPreface xxvAcknowledgment xxviiDisclaimer xxixNote for Readers xxxiIntroduction xxxiiiPart I Artificial Intelligence (AI) in Cybersecurity Analytics: Fundamental and Challenges 11 Analysis of Malicious Executables and Detection Techniques 3Geetika Munjal and Tushar Puri1.1 Introduction 31.2 Malicious Code Classification System 51.3 Literature Review 51.4 Malware Behavior Analysis 81.5 Conventional Detection Systems 111.6 Classifying Executables by Payload Function 121.7 Result and Discussion 131.8 Conclusion 152 Detection and Analysis of Botnet Attacks Using Machine Learning Techniques 19Supriya Raheja2.1 Introduction 192.2 Literature Review 202.3 Botnet Architecture 212.4 Methodology Adopted 242.5 Experimental Setup 272.6 Results and Discussions 282.7 Conclusion and Future Work 303 Artificial Intelligence Perspective on Digital Forensics 33Bhawna and Shilpa Mahajan3.1 Introduction 333.2 Literature Survey 343.3 Phases of Digital Forensics 353.4 Demystifying Artificial Intelligence in the DigitalWorld 363.5 Application of Machine Learning in Digital Forensics Investigations 393.6 Implementation of Artificial Intelligence in Forensics 403.7 Pattern Recognition Using Artificial Intelligence 403.8 Applications of AI in Criminal Investigations 423.9 Conclusion 434 Review on Machine Learning-based Traffic Rules Contravention Detection System 45Jahnavi and Urvashi4.1 Introduction 454.2 Technologies Involved in Smart Traffic Monitoring 474.3 Literature Review 504.4 Comparison of Results 594.5 Conclusion and Future Scope 595 Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies 63Vishwas Pitre, Ashish Joshi, Satya Saladi, and Suman Das5.1 Introduction 635.2 Literature Review 665.3 Proposed Methodology 675.4 Results 755.5 Conclusion and Future Scope ofWork 84Part II Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data 876 Malware Analysis Techniques in Android-Based Smartphone Applications 89Geetika Munjal, Avi Chakravarti, and Utkarsh Sharma6.1 Introduction 896.2 Malware Analysis Techniques 936.3 Hybrid Analysis 1026.4 Result 1026.5 Conclusion 1037 Cyber Threat Detection and Mitigation Using Artificial Intelligence -- A Cyber-physical Perspective 107Dalmo 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. Grata7.1 Introduction 1077.2 Types of Cyber Threats 1097.3 Cyber Threat Intelligence (CTI) 1167.4 Materials and Methods 1197.5 Cyber-Physical Systems Relying on AI (CPS-AI) 1217.6 Experimental Analysis 1267.7 Conclusion 1298 Performance Analysis of Intrusion Detection System Using ML Techniques 135Paridhi Pasrija, Utkarsh Singh, and Mehak Khurana8.1 Introduction 1358.2 Literature Survey 1368.3 ML Techniques 1378.4 Overview of Dataset 1408.5 Proposed Approach 1428.6 Simulation Results 1438.7 Conclusion and Future Work 1489 Spectral Pattern Learning Approach-based Student Sentiment Analysis Using Dense-net Multi Perception Neural Network in E-learning Environment 151Laishram Kirtibas Singh and R. Renuga Devi9.1 Introduction 1519.2 RelatedWork 1529.3 Proposed Implementation 1539.4 Result and Discussion 1599.5 Conclusion 16310 Big Data and Deep Learning-based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network 165Chingakham Nirma Devi and R. Renuga Devi10.1 Introduction 16510.2 RelatedWork 16610.3 Materials and Method 16810.4 Result and Discussion 17310.5 Conclusion 176Part III Applied Artificial Intelligence Approaches in Emerging Cybersecurity Domains 17911 Enhancing Security in Cloud Computing Using Artificial Intelligence (AI) 181Dalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Nikolaos Andreopoulos, Andrey Terziev, Anand Deshpande, Dhanashree Kulkarni, and Edwiges G.H. Grata11.1 Introduction 18111.2 Background 18411.3 Identification Function (IF) 18511.4 Protection Function (PF) 19111.5 Detection Function (DF) 19611.6 Response Function (RF) 20011.7 Recovery Function (RcF) 20511.8 Analysis, Discussion and Research Gaps 20511.9 Conclusion 20912 Utilization of Deep Learning Models for Safe Human-Friendly Computing in Cloud, Fog, and Mobile Edge Networks 221Diego M.R. Tudesco, Anand Deshpande, Asif A. Laghari, Abdullah A. Khan, Ricardo T. Lopes, R. Jenice Aroma, Kumudha Raimond, Lin Teng, and Asiya Khan12.1 Introduction 22112.2 Human-Centered Computing (HCC) 22312.3 Improving Cybersecurity Through Deep Learning (DL) Models: AI-HCC Systems 22912.5 Discussion 23812.6 Conclusion 23913 Artificial Intelligence for Threat Anomaly Detection Using Graph Databases -- A Semantic Outlook 249Edwiges G.H. Grata, Anand Deshpande, Ricardo T. Lopes, Asif A. Laghari, Abdullah A. Khan, R. Jenice Aroma, Kumudha Raimond, Shoulin Yin, and Awais Khan Jumani13.1 Introduction 24913.2 KGs in Cybersecurity 25213.3 CSKG Construction Methodologies 25413.3.1 CSKG Building Flow 25513.3.2 CS Ontology 25513.3.3 CS Entities Extraction 25613.3.4 Relations Extraction of CS Entities 25713.4 Datasets 25813.5 Application Scenarios 25913.5.1 CSA and Security Assessment 25913.5.2 CTs’ Discovery 26013.5.3 Attack Probing 26113.5.4 Clever Security Operation 26413.5.5 Smart Decision-Making 26513.5.6 Vulnerability Prediction and Supervision 26613.5.7 Malware Acknowledgment and Analysis 26713.5.8 Physical System Connection 26713.5.9 Supplementary Reasoning Tasks 26813.6 Discussion and Future Trends on CSKG 26913.7 Conclusion 27114 Security in Blockchain-Based Smart Cyber-Physical Applications Relying on Wireless Sensor and Actuators Networks 279Maria A. de Jesus, Asif A. Laghari, Abdullah A. Khan, Awais Khan Jumani, Mohammad Shabaz, Anand Deshpande, R. Jenice Aroma, Kumudha Raimond, and Asiya Khan14.1 Introduction 27914.2 Methodology 28214.3 GIBCS: An Overview 29214.4 Blockchain Layer 29414.5 Trust Management 29614.6 Blockchain for Secure Monitoring Back-End 29814.7 Blockchain-Enabled Cybersecurity: Discussion and Future Directions 30014.8 Conclusions 30115 Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data 311Keshav Kaushik15.1 Introduction to the IoT Security 31115.2 Role of Deep Learning in IoT Security 31615.3 Deep Learning Architecture for IoT Security 31915.4 Future Scope of Deep Learning in IoT Security 32215.5 Conclusion 323References 323Index 327