{"product_id":"applying-artificial-intelligence-in-cybersecurity-analytics-and-cyber-threat-detection-isbn-9781394196449","title":"Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection","description":"\u003cb\u003eAPPLYING ARTIFICIAL INTELLIGENCE \u003csmall\u003eIN\u003c\/small\u003e CYBERSECURITY ANALYTICS \u003csmall\u003eAND\u003c\/small\u003e CYBER THREAT DETECTION\u003c\/b\u003e \u003cp\u003e \u003cb\u003eComprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML)\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eis 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.  \u003c\/p\u003e\u003cp\u003eUsing 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. \u003c\/p\u003e\u003cp\u003eWith contributions from qualified authors with significant experience in the field, \u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eexplores topics such as: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis\u003c\/li\u003e\n\u003cli\u003eApplications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering\u003c\/li\u003e\n\u003cli\u003eHow AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats\u003c\/li\u003e\n\u003cli\u003eOffensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eProviding detailed coverage of a rapidly expanding field, \u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eis an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning. \u003c\/p\u003e\u003cp\u003eAbout the Editors xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003eAcknowledgment xxvii\u003c\/p\u003e \u003cp\u003eDisclaimer xxix\u003c\/p\u003e \u003cp\u003eNote for Readers xxxi\u003c\/p\u003e \u003cp\u003eIntroduction xxxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Artificial Intelligence (AI) in Cybersecurity Analytics: Fundamental and Challenges 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Analysis of Malicious Executables and Detection Techniques 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeetika Munjal and Tushar Puri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Malicious Code Classification System 5\u003c\/p\u003e \u003cp\u003e1.3 Literature Review 5\u003c\/p\u003e \u003cp\u003e1.4 Malware Behavior Analysis 8\u003c\/p\u003e \u003cp\u003e1.5 Conventional Detection Systems 11\u003c\/p\u003e \u003cp\u003e1.6 Classifying Executables by Payload Function 12\u003c\/p\u003e \u003cp\u003e1.7 Result and Discussion 13\u003c\/p\u003e \u003cp\u003e1.8 Conclusion 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Detection and Analysis of Botnet Attacks Using Machine Learning Techniques 19\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSupriya Raheja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 19\u003c\/p\u003e \u003cp\u003e2.2 Literature Review 20\u003c\/p\u003e \u003cp\u003e2.3 Botnet Architecture 21\u003c\/p\u003e \u003cp\u003e2.4 Methodology Adopted 24\u003c\/p\u003e \u003cp\u003e2.5 Experimental Setup 27\u003c\/p\u003e \u003cp\u003e2.6 Results and Discussions 28\u003c\/p\u003e \u003cp\u003e2.7 Conclusion and Future Work 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Artificial Intelligence Perspective on Digital Forensics 33\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhawna and Shilpa Mahajan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 33\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 34\u003c\/p\u003e \u003cp\u003e3.3 Phases of Digital Forensics 35\u003c\/p\u003e \u003cp\u003e3.4 Demystifying Artificial Intelligence in the DigitalWorld 36\u003c\/p\u003e \u003cp\u003e3.5 Application of Machine Learning in Digital Forensics Investigations 39\u003c\/p\u003e \u003cp\u003e3.6 Implementation of Artificial Intelligence in Forensics 40\u003c\/p\u003e \u003cp\u003e3.7 Pattern Recognition Using Artificial Intelligence 40\u003c\/p\u003e \u003cp\u003e3.8 Applications of AI in Criminal Investigations 42\u003c\/p\u003e \u003cp\u003e3.9 Conclusion 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Review on Machine Learning-based Traffic Rules Contravention Detection System 45\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJahnavi and Urvashi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 45\u003c\/p\u003e \u003cp\u003e4.2 Technologies Involved in Smart Traffic Monitoring 47\u003c\/p\u003e \u003cp\u003e4.3 Literature Review 50\u003c\/p\u003e \u003cp\u003e4.4 Comparison of Results 59\u003c\/p\u003e \u003cp\u003e4.5 Conclusion and Future Scope 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Enhancing Cybersecurity Ratings Using Artificial Intelligence and DevOps Technologies 63\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVishwas Pitre, Ashish Joshi, Satya Saladi, and Suman Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 63\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 66\u003c\/p\u003e \u003cp\u003e5.3 Proposed Methodology 67\u003c\/p\u003e \u003cp\u003e5.4 Results 75\u003c\/p\u003e \u003cp\u003e5.5 Conclusion and Future Scope ofWork 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Cyber Threat Detection and Analysis Using Artificial Intelligence and Big Data 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Malware Analysis Techniques in Android-Based Smartphone Applications 89\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeetika Munjal, Avi Chakravarti, and Utkarsh Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 89\u003c\/p\u003e \u003cp\u003e6.2 Malware Analysis Techniques 93\u003c\/p\u003e \u003cp\u003e6.3 Hybrid Analysis 102\u003c\/p\u003e \u003cp\u003e6.4 Result 102\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Cyber Threat Detection and Mitigation Using Artificial Intelligence -- A Cyber-physical Perspective 107\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDalmo 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 107\u003c\/p\u003e \u003cp\u003e7.2 Types of Cyber Threats 109\u003c\/p\u003e \u003cp\u003e7.3 Cyber Threat Intelligence (CTI) 116\u003c\/p\u003e \u003cp\u003e7.4 Materials and Methods 119\u003c\/p\u003e \u003cp\u003e7.5 Cyber-Physical Systems Relying on AI (CPS-AI) 121\u003c\/p\u003e \u003cp\u003e7.6 Experimental Analysis 126\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Performance Analysis of Intrusion Detection System Using ML Techniques 135\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eParidhi Pasrija, Utkarsh Singh, and Mehak Khurana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 135\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 136\u003c\/p\u003e \u003cp\u003e8.3 ML Techniques 137\u003c\/p\u003e \u003cp\u003e8.4 Overview of Dataset 140\u003c\/p\u003e \u003cp\u003e8.5 Proposed Approach 142\u003c\/p\u003e \u003cp\u003e8.6 Simulation Results 143\u003c\/p\u003e \u003cp\u003e8.7 Conclusion and Future Work 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Spectral Pattern Learning Approach-based Student Sentiment Analysis Using Dense-net Multi Perception Neural Network in E-learning Environment 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLaishram Kirtibas Singh and R. Renuga Devi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 151\u003c\/p\u003e \u003cp\u003e9.2 RelatedWork 152\u003c\/p\u003e \u003cp\u003e9.3 Proposed Implementation 153\u003c\/p\u003e \u003cp\u003e9.4 Result and Discussion 159\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Big Data and Deep Learning-based Tourism Industry Sentiment Analysis Using Deep Spectral Recurrent Neural Network 165\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChingakham Nirma Devi and R. Renuga Devi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 165\u003c\/p\u003e \u003cp\u003e10.2 RelatedWork 166\u003c\/p\u003e \u003cp\u003e10.3 Materials and Method 168\u003c\/p\u003e \u003cp\u003e10.4 Result and Discussion 173\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Applied Artificial Intelligence Approaches in Emerging Cybersecurity Domains 179\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Enhancing Security in Cloud Computing Using Artificial Intelligence (AI) 181\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDalmo Stutz, Joaquim T. de Assis, Asif A. Laghari, Abdullah A. Khan, Nikolaos Andreopoulos, Andrey Terziev, Anand Deshpande, Dhanashree Kulkarni, and Edwiges G.H. Grata\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 181\u003c\/p\u003e \u003cp\u003e11.2 Background 184\u003c\/p\u003e \u003cp\u003e11.3 Identification Function (IF) 185\u003c\/p\u003e \u003cp\u003e11.4 Protection Function (PF) 191\u003c\/p\u003e \u003cp\u003e11.5 Detection Function (DF) 196\u003c\/p\u003e \u003cp\u003e11.6 Response Function (RF) 200\u003c\/p\u003e \u003cp\u003e11.7 Recovery Function (RcF) 205\u003c\/p\u003e \u003cp\u003e11.8 Analysis, Discussion and Research Gaps 205\u003c\/p\u003e \u003cp\u003e11.9 Conclusion 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Utilization of Deep Learning Models for Safe Human-Friendly Computing in Cloud, Fog, and Mobile Edge Networks 221\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDiego M.R. Tudesco, Anand Deshpande, Asif A. Laghari, Abdullah A. Khan, Ricardo T. Lopes, R. Jenice Aroma, Kumudha Raimond, Lin Teng, and Asiya Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 221\u003c\/p\u003e \u003cp\u003e12.2 Human-Centered Computing (HCC) 223\u003c\/p\u003e \u003cp\u003e12.3 Improving Cybersecurity Through Deep Learning (DL) Models: AI-HCC Systems 229\u003c\/p\u003e \u003cp\u003e12.5 Discussion 238\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Artificial Intelligence for Threat Anomaly Detection Using Graph Databases -- A Semantic Outlook 249\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEdwiges 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 249\u003c\/p\u003e \u003cp\u003e13.2 KGs in Cybersecurity 252\u003c\/p\u003e \u003cp\u003e13.3 CSKG Construction Methodologies 254\u003c\/p\u003e \u003cp\u003e13.3.1 CSKG Building Flow 255\u003c\/p\u003e \u003cp\u003e13.3.2 CS Ontology 255\u003c\/p\u003e \u003cp\u003e13.3.3 CS Entities Extraction 256\u003c\/p\u003e \u003cp\u003e13.3.4 Relations Extraction of CS Entities 257\u003c\/p\u003e \u003cp\u003e13.4 Datasets 258\u003c\/p\u003e \u003cp\u003e13.5 Application Scenarios 259\u003c\/p\u003e \u003cp\u003e13.5.1 CSA and Security Assessment 259\u003c\/p\u003e \u003cp\u003e13.5.2 CTs’ Discovery 260\u003c\/p\u003e \u003cp\u003e13.5.3 Attack Probing 261\u003c\/p\u003e \u003cp\u003e13.5.4 Clever Security Operation 264\u003c\/p\u003e \u003cp\u003e13.5.5 Smart Decision-Making 265\u003c\/p\u003e \u003cp\u003e13.5.6 Vulnerability Prediction and Supervision 266\u003c\/p\u003e \u003cp\u003e13.5.7 Malware Acknowledgment and Analysis 267\u003c\/p\u003e \u003cp\u003e13.5.8 Physical System Connection 267\u003c\/p\u003e \u003cp\u003e13.5.9 Supplementary Reasoning Tasks 268\u003c\/p\u003e \u003cp\u003e13.6 Discussion and Future Trends on CSKG 269\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Security in Blockchain-Based Smart Cyber-Physical Applications Relying on Wireless Sensor and Actuators Networks 279\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaria A. de Jesus, Asif A. Laghari, Abdullah A. Khan, Awais Khan Jumani, Mohammad Shabaz, Anand Deshpande, R. Jenice Aroma, Kumudha Raimond, and Asiya Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 279\u003c\/p\u003e \u003cp\u003e14.2 Methodology 282\u003c\/p\u003e \u003cp\u003e14.3 GIBCS: An Overview 292\u003c\/p\u003e \u003cp\u003e14.4 Blockchain Layer 294\u003c\/p\u003e \u003cp\u003e14.5 Trust Management 296\u003c\/p\u003e \u003cp\u003e14.6 Blockchain for Secure Monitoring Back-End 298\u003c\/p\u003e \u003cp\u003e14.7 Blockchain-Enabled Cybersecurity: Discussion and Future Directions 300\u003c\/p\u003e \u003cp\u003e14.8 Conclusions 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Leveraging Deep Learning Techniques for Securing the Internet of Things in the Age of Big Data 311\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKeshav Kaushik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction to the IoT Security 311\u003c\/p\u003e \u003cp\u003e15.2 Role of Deep Learning in IoT Security 316\u003c\/p\u003e \u003cp\u003e15.3 Deep Learning Architecture for IoT Security 319\u003c\/p\u003e \u003cp\u003e15.4 Future Scope of Deep Learning in IoT Security 322\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 323\u003c\/p\u003e \u003cp\u003eReferences 323\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eShilpa Mahajan\u003c\/b\u003e, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMehak Khurana\u003c\/b\u003e, PhD, is an Associate Professor in the School of Engineering and Technology at The NorthCap University, India.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVania Vieira Estrela\u003c\/b\u003e, PhD, is a Professor with the Telecommunications Department of the Fluminense Federal University, Brazil.   \u003c\/p\u003e\u003cp\u003e \u003cb\u003eComprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML)\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eis 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.  \u003c\/p\u003e\u003cp\u003eUsing 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. \u003c\/p\u003e\u003cp\u003eWith contributions from qualified authors with significant experience in the field, \u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eexplores topics such as: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis\u003c\/li\u003e\n\u003cli\u003eApplications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering\u003c\/li\u003e\n\u003cli\u003eHow AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats\u003c\/li\u003e\n\u003cli\u003eOffensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eProviding detailed coverage of a rapidly expanding field, \u003ci\u003eApplying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection \u003c\/i\u003eis an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988754874597,"sku":"NP9781394196449","price":125.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394196449.jpg?v=1761781461","url":"https:\/\/k12savings.com\/es\/products\/applying-artificial-intelligence-in-cybersecurity-analytics-and-cyber-threat-detection-isbn-9781394196449","provider":"K12savings","version":"1.0","type":"link"}