Artificial Intelligence and Data Mining Approaches in Security Frameworks

Neeraj Bhargava, PhD, is a professor and head of the Department of Computer Science at Maharshi Dayanand Saraswati University in Ajmer, India, having earned his doctorate from the University of Rajasthan, Jaipur in India. He has over 30 years of teaching experience at the university level and has contributed to numerous books throughout his career. He has also published over 100 papers in scientific and technical journals and has been an organizing chair on over 15 scientific conferences. His work on face recognition and fingerprint recognition is often cited in other research and is well-known all over the world.

ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKSWritten and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts.Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner.This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice.This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library.This groundbreaking new volume:* Clarifies the understanding of certain key mechanisms of technology helpful in the use of artificial intelligence and data mining in security frameworks* Covers practical approaches to the problems engineers face in working in this field, focusing on the applications used every day* Contains numerous examples, offering critical solutions to engineers and scientists* Presents these new applications of AI and data mining that are of prime importance to human civilization as a whole

Preface xiii1 Role of AI in Cyber Security 1Navani Siroya and Prof Manju Mandot1.1 Introduction 21.2 Need for Artificial Intelligence 21.3 Artificial Intelligence in Cyber Security 31.3.1 Multi-Layered Security System Design 31.3.2 Traditional Security Approach and AI 41.4 Related Work 51.4.1 Literature Review 51.4.2 Corollary 61.5 Proposed Work 61.5.1 System Architecture 71.5.2 Future Scope 71.6 Conclusion 7References 82 Privacy Preserving Using Data Mining 11Chitra Jalota and Dr. Rashmi Agrawal2.1 Introduction 112.2 Data Mining Techniques and Their Role in Classification and Detection 142.3 Clustering 192.4 Privacy Preserving Data Mining (PPDM) 212.5 Intrusion Detection Systems (IDS) 222.5.1 Types of IDS 232.5.1.1 Network-Based IDS 232.5.1.2 Host-Based IDS 242.5.1.3 Hybrid IDS 252.6 Phishing Website Classification 262.7 Attacks by Mitigating Code Injection 272.7.1 Code Injection and Its Categories 272.8 Conclusion 28References 293 Role of Artificial Intelligence in Cyber Security and Security Framework 33Shweta Sharma3.1 Introduction 343.2 AI for Cyber Security 363.3 Uses of Artificial Intelligence in Cyber Security 383.4 The Role of AI in Cyber Security 403.4.1 Simulated Intelligence Can Distinguish Digital Assaults 413.4.2 Computer-Based Intelligence Can Forestall Digital Assaults 423.4.3 Artificial Intelligence and Huge Scope Cyber Security 423.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security 433.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence 443.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML) 453.4.7 AI Adopters Moving to Make a Move 453.5 AI Impacts on Cyber Security 463.6 The Positive Uses of AI Based for Cyber Security 483.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security 493.8 Solutions to Artificial Intelligence Confinements 503.9 Security Threats of Artificial Intelligence 513.10 Expanding Cyber Security Threats with Artificial Consciousness 523.11 Artificial Intelligence in Cybersecurity - Current Use-Cases and Capabilities 553.11.1 AI for System Danger Distinguishing Proof 563.11.2 The Common Fit for Artificial Consciousness in Cyber Security 563.11.3 Artificial Intelligence for System Danger ID 573.11.4 Artificial Intelligence Email Observing 583.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers 583.11.6 The Fate of Computer-Based Intelligence in Cyber Security 593.12 How to Improve Cyber Security for Artificial Intelligence 603.13 Conclusion 61References 624 Botnet Detection Using Artificial Intelligence 65Astha Parihar and Prof. Neeraj Bhargava4.1 Introduction to Botnet 664.2 Botnet Detection 674.2.1 Host-Centred Detection (HCD) 684.2.2 Honey Nets-Based Detection (HNBD) 694.2.3 Network-Based Detection (NBD) 694.3 Botnet Architecture 694.3.1 Federal Model 704.3.1.1 IBN-Based Protocol 714.3.1.2 HTTP-Based Botnets 714.3.2 Devolved Model 714.3.3 Cross Model 724.4 Detection of Botnet 734.4.1 Perspective of Botnet Detection 734.4.2 Detection (Disclosure) Technique 734.4.3 Region of Tracing 744.5 Machine Learning 744.5.1 Machine Learning Characteristics 744.6 A Machine Learning Approach of Botnet Detection 754.7 Methods of Machine Learning Used in Botnet Exposure 764.7.1 Supervised (Administrated) Learning 764.7.1.1 Appearance of Supervised Learning 774.7.2 Unsupervised Learning 784.7.2.1 Role of Unsupervised Learning 794.8 Problems with Existing Botnet Detection Systems 804.9 Extensive Botnet Detection System (EBDS) 814.10 Conclusion 83References 845 Spam Filtering Using AI 87Yojna Khandelwal and Dr. Ritu Bhargava5.1 Introduction 875.1.1 What is SPAM? 875.1.2 Purpose of Spamming 885.1.3 Spam Filters Inputs and Outputs 885.2 Content-Based Spam Filtering Techniques 895.2.1 Previous Likeness-Based Filters 895.2.2 Case-Based Reasoning Filters 895.2.3 Ontology-Based E-Mail Filters 905.2.4 Machine-Learning Models 905.2.4.1 Supervised Learning 905.2.4.2 Unsupervised Learning 905.2.4.3 Reinforcement Learning 915.3 Machine Learning-Based Filtering 915.3.1 Linear Classifiers 915.3.2 Naïve Bayes Filtering 925.3.3 Support Vector Machines 945.3.4 Neural Networks and Fuzzy Logics-Based Filtering 945.4 Performance Analysis 975.5 Conclusion 97References 986 Artificial Intelligence in the Cyber Security Environment 101Jaya Jain6.1 Introduction 1026.2 Digital Protection and Security Correspondences Arrangements 1046.2.1 Operation Safety and Event Response 1056.2.2 AI2 1056.2.2.1 CylanceProtect 1056.3 Black Tracking 1066.3.1 Web Security 1076.3.1.1 Amazon Macie 1086.4 Spark Cognition Deep Military 1106.5 The Process of Detecting Threats 1116.6 Vectra Cognito Networks 1126.7 Conclusion 115References 1157 Privacy in Multi-Tenancy Frameworks Using AI 119Shweta Solanki7.1 Introduction 1197.2 Framework of Multi-Tenancy 1207.3 Privacy and Security in Multi-Tenant Base System Using AI 1227.4 Related Work 1257.5 Conclusion 125References 1268 Biometric Facial Detection and Recognition Based on ILPB and SVM 129Shubhi Srivastava, Ankit Kumar and Shiv Prakash8.1 Introduction 1298.1.1 Biometric 1318.1.2 Categories of Biometric 1318.1.2.1 Advantages of Biometric 1328.1.3 Significance and Scope 1328.1.4 Biometric Face Recognition 1328.1.5 Related Work 1368.1.6 Main Contribution 1368.1.7 Novelty Discussion 1378.2 The Proposed Methodolgy 1398.2.1 Face Detection Using Haar Algorithm 1398.2.2 Feature Extraction Using ILBP 1418.2.3 Dataset 1438.2.4 Classification Using SVM 1438.3 Experimental Results 1458.3.1 Face Detection 1468.3.2 Feature Extraction 1468.3.3 Recognize Face Image 1478.4 Conclusion 151References 1529 Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT 155S K Rajesh Kanna, O. Pandithurai, N. Anand, P. Sethuramalingam and Abdul Munaf9.1 Introduction 1569.2 Inspection System for Defect Detection 1589.3 Defect Recognition Methodology 1629.4 Health Care MGPS Inspection 1659.5 Conclusion 168References 16910 Fuzzy Approach for Designing Security Framework 173Kapil Chauhan10.1 Introduction 17310.2 Fuzzy Set 17710.3 Planning for a Rule-Based Expert System for Cyber Security 18510.3.1 Level 1: Defining Cyber Security Expert System Variables 18510.3.2 Level 2: Information Gathering for Cyber Terrorism 18510.3.3 Level 3: System Design 18610.3.4 Level 4: Rule-Based Model 18710.4 Digital Security 18810.4.1 Cyber-Threats 18810.4.2 Cyber Fault 18810.4.3 Different Types of Security Services 18910.5 Improvement of Cyber Security System (Advance) 19010.5.1 Structure 19010.5.2 Cyber Terrorism for Information/Data Collection 19110.6 Conclusions 191References 19211 Threat Analysis Using Data Mining Technique 197Riddhi Panchal and Binod Kumar11.1 Introduction 19811.2 Related Work 19911.3 Data Mining Methods in Favor of Cyber-Attack Detection 20111.4 Process of Cyber-Attack Detection Based on Data Mining 20411.5 Conclusion 205References 20512 Intrusion Detection Using Data Mining 209Astha Parihar and Pramod Singh Rathore12.1 Introduction 20912.2 Essential Concept 21012.2.1 Intrusion Detection System 21112.2.2 Categorization of IDS 21212.2.2.1 Web Intrusion Detection System (WIDS) 21312.2.2.2 Host Intrusion Detection System (HIDS) 21412.2.2.3 Custom-Based Intrusion Detection System (CIDS) 21512.2.2.4 Application Protocol-Based Intrusion Detection System (APIDS) 21512.2.2.5 Hybrid Intrusion Detection System 21612.3 Detection Program 21612.3.1 Misuse Detection 21712.3.1.1 Expert System 21712.3.1.2 Stamp Analysis 21812.3.1.3 Data Mining 22012.4 Decision Tree 22112.4.1 Classification and Regression Tree (CART) 22212.4.2 Iterative Dichotomise 3 (ID3) 22212.4.3 C 4.5 22312.5 Data Mining Model for Detecting the Attacks 22312.5.1 Framework of the Technique 22412.6 Conclusion 226References 22613 A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT 229S K Rajesh Kanna, V. Nagaraju, D. Jayashree, Abdul Munaf and M. Ashok13.1 Introduction 23013.2 Literature Survey 23113.3 Experimental Framework 23213.4 Healthcare Monitoring 23713.5 Results and Discussion 24013.6 Conclusion 242References 24314 Vision-Based Gesture Recognition: A Critical Review 247Neela Harish, Praveen, Prasanth, Aparna and Athaf14.1 Introduction 24714.2 Issues in Vision-Based Gesture Recognition 24814.2.1 Based on Gestures 24914.2.2 Based on Performance 24914.2.3 Based on Background 24914.3 Step-by-Step Process in Vision-Based 24914.3.1 Sensing 25114.3.2 Preprocessing 25214.3.3 Feature Extraction 25214.4 Classification 25314.5 Literature Review 25414.6 Conclusion 258References 25815 SPAM Filtering Using Artificial Intelligence 261Abha Jain15.1 Introduction 26115.2 Architecture of Email Servers and Email Processing Stages 26515.2.1 Architecture - Email Spam Filtering 26515.2.1.1 Spam Filter - Gmail 26615.2.1.2 Mail Filter Spam - Yahoo 26615.2.1.3 Email Spam Filter - Outlook 26715.2.2 Email Spam Filtering - Process 26715.2.2.1 Pre-Handling 26815.2.2.2 Taxation 26815.2.2.3 Election of Features 26815.2.3 Freely Available Email Spam Collection 26915.3 Execution Evaluation Measures 26915.4 Classification - Machine Learning Technique for Email Spam 27515.4.1 Flock Technique - Clustering 27515.4.2 Naïve Bayes Classifier 27615.4.3 Neural Network 27915.4.4 Firefly Algorithm 28215.4.5 Fuzzy Set Classifiers 28315.4.6 Support Vector Machine 28415.4.7 Decision Tree 28615.4.7.1 NBTree Classifier 28615.4.7.2 C4.5/J48 Decision Tree Algorithm 28715.4.7.3 Logistic Version Tree Induction (LVT) 28715.4.8 Ensemble Classifiers 28815.4.9 Random Forests (RF) 28915.5 Conclusion 290References 290Index 295