Artificial intelligence and data mining approaches in security frameworks. (2021)

• Record Type: Book
• Further Information: Note: Edited by Neeraj Bhargava, Ritu Bhargava, Pramod Singh Rathore, Rashmi Agrawal.
• Editors: Bhargava, Neeraj; Bhargava, Ritu; Rathore, Pramod Singh, 1988-; Agrawal, Rashmi, 1978-
• Contents: 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
• Edition: 1st
• Publisher Details: Hoboken : Wiley-Scrivener
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 006.3; Artificial intelligence; Data mining; Computer security
• Languages: English
• ISBNs: 9781119760436
• Related ISBNs: 9781119760405
• Notes: Note: Description based on CIP data; resource not viewed.
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