Preface xiii -- 1 Role of AI in Cyber Security 1 Navani Siroya and Prof Manju Mandot -- 1.1 Introduction 2 -- 1.2 Need for Artificial Intelligence 2 -- 1.3 Artificial Intelligence in Cyber Security 3 -- 1.3.1 Multi-Layered Security System Design 3 -- 1.3.2 Traditional Security Approach and AI 4 -- 1.4 Related Work 5 -- 1.4.1 Literature Review 5 -- 1.4.2 Corollary 6 -- 1.5 Proposed Work 6 -- 1.5.1 System Architecture 7 -- 1.5.2 Future Scope 7 -- 1.6 Conclusion 7 -- References 8 -- 2 Privacy Preserving Using Data Mining 11 Chitra Jalota and Dr. Rashmi Agrawal -- 2.1 Introduction 11 -- 2.2 Data Mining Techniques and Their Role in Classification and Detection 14 -- 2.3 Clustering 19 -- 2.4 Privacy Preserving Data Mining (PPDM) 21 -- 2.5 Intrusion Detection Systems (IDS) 22 -- 2.5.1 Types of IDS 23 -- 2.5.1.1 Network-Based IDS 23 -- 2.5.1.2 Host-Based IDS 24 -- 2.5.1.3 Hybrid IDS 25 -- 2.6 Phishing Website Classification 26 -- 2.7 Attacks by Mitigating Code Injection 27 -- 2.7.1 Code Injection and Its Categories 27 -- 2.8 Conclusion 28 -- References 29 -- 3 Role of Artificial Intelligence in Cyber Security and Security Framework 33 Shweta Sharma -- 3.1 Introduction 34 -- 3.2 AI for Cyber Security 36 -- 3.3 Uses of Artificial Intelligence in Cyber Security 38 -- 3.4 The Role of AI in Cyber Security 40 -- 3.4.1 Simulated Intelligence Can Distinguish Digital Assaults 41 -- 3.4.2 Computer-Based Intelligence Can Forestall Digital Assaults 42 -- 3.4.3 Artificial Intelligence and Huge Scope Cyber Security 42 -- 3.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security 43 -- 3.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence 44 -- 3.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML) 45 -- 3.4.7 AI Adopters Moving to Make a Move 45 -- 3.5 AI Impacts on Cyber Security 46 -- 3.6 The Positive Uses of AI Based for Cyber Security 48 -- 3.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security 49 -- 3.8 Solutions to Artificial Intelligence Confinements 50 -- 3.9 Security Threats of Artificial Intelligence 51 -- 3.10 Expanding Cyber Security Threats with Artificial Consciousness 52 -- 3.11 Artificial Intelligence in Cybersecurity – Current Use-Cases and Capabilities 55 -- 3.11.1 AI for System Danger Distinguishing Proof 56 -- 3.11.2 The Common Fit for Artificial Consciousness in Cyber Security 56 -- 3.11.3 Artificial Intelligence for System Danger ID 57 -- 3.11.4 Artificial Intelligence Email Observing 58 -- 3.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers 58 -- 3.11.6 The Fate of Computer-Based Intelligence in Cyber Security 59 -- 3.12 How to Improve Cyber Security for Artificial Intelligence 60 -- 3.13 Conclusion 61 -- References 62 -- 4 Botnet Detection Using Artificial Intelligence 65 Astha Parihar and Prof. Neeraj Bhargava -- 4.1 Introduction to Botnet 66 -- 4.2 Botnet Detection 67 -- 4.2.1 Host-Centred Detection (HCD) 68 -- 4.2.2 Honey Nets-Based Detection (HNBD) 69 -- 4.2.3 Network-Based Detection (NBD) 69 -- 4.3 Botnet Architecture 69 -- 4.3.1 Federal Model 70 -- 4.3.1.1 IBN-Based Protocol 71 -- 4.3.1.2 HTTP-Based Botnets 71 -- 4.3.2 Devolved Model 71 -- 4.3.3 Cross Model 72 -- 4.4 Detection of Botnet 73 -- 4.4.1 Perspective of Botnet Detection 73 -- 4.4.2 Detection (Disclosure) Technique 73 -- 4.4.3 Region of Tracing 74 -- 4.5 Machine Learning 74 -- 4.5.1 Machine Learning Characteristics 74 -- 4.6 A Machine Learning Approach of Botnet Detection 75 -- 4.7 Methods of Machine Learning Used in Botnet Exposure 76 -- 4.7.1 Supervised (Administrated) Learning 76 -- 4.7.1.1 Appearance of Supervised Learning 77 -- 4.7.2 Unsupervised Learning 78 -- 4.7.2.1 Role of Unsupervised Learning 79 -- 4.8 Problems with Existing Botnet Detection Systems 80 -- 4.9 Extensive Botnet Detection System (EBDS) 81 -- 4.10 Conclusion 83 -- References 84 -- 5 Spam Filtering Using AI 87 Yojna Khandelwal and Dr. Ritu Bhargava -- 5.1 Introduction 87 -- 5.1.1 What is SPAM? 87 -- 5.1.2 Purpose of Spamming 88 -- 5.1.3 Spam Filters Inputs and Outputs 88 -- 5.2 Content-Based Spam Filtering Techniques 89 -- 5.2.1 Previous Likeness–Based Filters 89 -- 5.2.2 Case-Based Reasoning Filters 89 -- 5.2.3 Ontology-Based E-Mail Filters 90 -- 5.2.4 Machine-Learning Models 90 -- 5.2.4.1 Supervised Learning 90 -- 5.2.4.2 Unsupervised Learning 90 -- 5.2.4.3 Reinforcement Learning 91 -- 5.3 Machine Learning–Based Filtering 91 -- 5.3.1 Linear Classifiers 91 -- 5.3.2 Naïve Bayes Filtering 92 -- 5.3.3 Support Vector Machines 94 -- 5.3.4 Neural Networks and Fuzzy Logics–Based Filtering 94 -- 5.4 Performance Analysis 97 -- 5.5 Conclusion 97 -- References 98 -- 6 Artificial Intelligence in the Cyber Security Environment 101 Jaya Jain -- 6.1 Introduction 102 -- 6.2 Digital Protection and Security Correspondences Arrangements 104 -- 6.2.1 Operation Safety and Event Response 105 -- 6.2.2 AI2 105 -- 6.2.2.1 CylanceProtect 105 -- 6.3 Black Tracking 106 -- 6.3.1 Web Security 107 -- 6.3.1.1 Amazon Macie 108 -- 6.4 Spark Cognition Deep Military 110 -- 6.5 The Process of Detecting Threats 111 -- 6.6 Vectra Cognito Networks 112 -- 6.7 Conclusion 115 -- References 115 -- 7 Privacy in Multi-Tenancy Frameworks Using AI 119 Shweta Solanki -- 7.1 Introduction 119 -- 7.2 Framework of Multi-Tenancy 120 -- 7.3 Privacy and Security in Multi-Tenant Base System Using AI 122 -- 7.4 Related Work 125 -- 7.5 Conclusion 125 -- References 126 -- 8 Biometric Facial Detection and Recognition Based on ILPB and SVM 129 Shubhi Srivastava, Ankit Kumar and Shiv Prakash -- 8.1 Introduction 129 -- 8.1.1 Biometric 131 -- 8.1.2 Categories of Biometric 131 -- 8.1.2.1 Advantages of Biometric 132 -- 8.1.3 Significance and Scope 132 -- 8.1.4 Biometric Face Recognition 132 -- 8.1.5 Related Work 136 -- 8.1.6 Main Contribution 136 -- 8.1.7 Novelty Discussion 137 -- 8.2 The Proposed Methodolgy 139 -- 8.2.1 Face Detection Using Haar Algorithm 139 -- 8.2.2 Feature Extraction Using ILBP 141 -- 8.2.3 Dataset 143 -- 8.2.4 Classification Using SVM 143 -- 8.3 Experimental Results 145 -- 8.3.1 Face Detection 146 -- 8.3.2 Feature Extraction 146 -- 8.3.3 Recognize Face Image 147 -- 8.4 Conclusion 151 -- References 152 -- 9 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155 S K Rajesh Kanna, O. Pandithurai, N. Anand, P.

Sethuramalingam and Abdul Munaf -- 9.1 Introduction 156 -- 9.2 Inspection System for Defect Detection 158 -- 9.3 Defect Recognition Methodology 162 -- 9.4 Health Care MGPS Inspection 165 -- 9.5 Conclusion 168 -- References 169 -- 10 Fuzzy Approach for Designing Security Framework 173 Kapil Chauhan -- 10.1 Introduction 173 -- 10.2 Fuzzy Set 177 -- 10.3 Planning for a Rule-Based Expert System for Cyber Security 185 -- 10.3.1 Level 1: Defining Cyber Security Expert System Variables 185 -- 10.3.2 Level 2: Information Gathering for Cyber Terrorism 185 -- 10.3.3 Level 3: System Design 186 -- 10.3.4 Level 4: Rule-Based Model 187 -- 10.4 Digital Security 188 -- 10.4.1 Cyber-Threats 188 -- 10.4.2 Cyber Fault 188 -- 10.4.3 Different Types of Security Services 189 -- 10.5 Improvement of Cyber Security System (Advance) 190 -- 10.5.1 Structure 190 -- 10.5.2 Cyber Terrorism for Information/Data Collection 191 -- 10.6 Conclusions 191 -- References 192 -- 11 Threat Analysis Using Data Mining Technique 197 Riddhi Panchal and Binod Kumar -- 11.1 Introduction 198 -- 11.2 Related Work 199 -- 11.3 Data Mining Methods in Favor of Cyber-Attack Detection 201 -- 11.4 Process of Cyber-Attack Detection Based on Data Mining 204 -- 11.5 Conclusion 205 -- References 205 -- 12 Intrusion Detection Using Data Mining 209 Astha Parihar and Pramod Singh Rathore -- 12.1 Introduction 209 -- 12.2 Essential Concept 210 -- 12.2.1 Intrusion Detection System 211 -- 12.2.2 Categorization of IDS 212 -- 12.2.2.1 Web Intrusion Detection System (WIDS) 213 -- 12.2.2.2 Host Intrusion Detection System (HIDS) 214 -- 12.2.2.3 Custom-Based Intrusion Detection System (CIDS) 215 -- 12.2.2.4 Application Protocol-Based Intrusion Detection System (APIDS) 215 -- 12.2.2.5 Hybrid Intrusion Detection System 216 -- 12.3 Detection Program 216 -- 12.3.1 Misuse Detection 217 -- 12.3.1.1 Expert System 217 -- 12.3.1.2 Stamp Analysis 218 -- 12.3.1.3 Data Mining 220 -- 12.4 Decision Tree 221 -- 12.4.1 Classification and Regression Tree (CART) 222 -- 12.4.2 Iterative Dichotomise 3 (ID3) 222 -- 12.4.3 C 4.5 223 -- 12.5 Data Mining Model for Detecting the Attacks 223 -- 12.5.1 Framework of the Technique 224 -- 12.6 Conclusion 226 -- References 226 -- 13 A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT 229 S K Rajesh Kanna, V. Nagaraju, D. Jayashree, Abdul Munaf and M. Ashok -- 13.1 Introduction 230 -- 13.2 Literature Survey 231 -- 13.3 Experimental Framework 232 -- 13.4 Healthcare Monitoring 237 -- 13.5 Results and Discussion 240 -- 13.6 Conclusion 242 -- ...