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Группа авторов. Artificial Intelligence and Data Mining Approaches in Security Frameworks
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
rtificial Intelligence and Data Mining Approaches in Security Frameworks
Preface
1. Role of AI in Cyber Security
1.1 Introduction
1.2 Need for Artificial Intelligence
1.3 Artificial Intelligence in Cyber Security
1.3.1 Multi-Layered Security System Design
1.3.2 Traditional Security Approach and AI
1.4 Related Work. 1.4.1 Literature Review
1.4.2 Corollary
1.5 Proposed Work
1.5.1 System Architecture
1.5.2 Future Scope
1.6 Conclusion
References
2. Privacy Preserving Using Data Mining
2.1 Introduction
2.2 Data Mining Techniques and Their Role in Classification and Detection
2.3 Clustering
2.4 Privacy Preserving Data Mining (PPDM)
2.5 Intrusion Detection Systems (IDS)
2.5.1 Types of IDS
2.5.1.1 Network-Based IDS
2.5.1.2 Host-Based IDS
2.5.1.3 Hybrid IDS
2.6 Phishing Website Classification
2.7 Attacks by Mitigating Code Injection
2.7.1 Code Injection and Its Categories
2.8 Conclusion
References
3. Role of Artificial Intelligence in Cyber Security and Security Framework
3.1 Introduction
3.2 AI for Cyber Security
3.3 Uses of Artificial Intelligence in Cyber Security
3.4 The Role of AI in Cyber Security
3.4.1 Simulated Intelligence Can Distinguish Digital Assaults
3.4.2 Computer-Based Intelligence Can Forestall Digital Assaults
3.4.3 Artificial Intelligence and Huge Scope Cyber Security
3.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security
3.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence
3.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML)
3.4.7 AI Adopters Moving to Make a Move
3.5 AI Impacts on Cyber Security
3.6 The Positive Uses of AI Based for Cyber Security
3.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security
3.8 Solutions to Artificial Intelligence Confinements
3.9 Security Threats of Artificial Intelligence
3.10 Expanding Cyber Security Threats with Artificial Consciousness
3.11 Artificial Intelligence in Cybersecurity – Current Use-Cases and Capabilities
3.11.1 AI for System Danger Distinguishing Proof
3.11.2 The Common Fit for Artificial Consciousness in Cyber Security
3.11.3 Artificial Intelligence for System Danger ID
3.11.4 Artificial Intelligence Email Observing
3.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers
3.11.6 The Fate of Computer-Based Intelligence in Cyber Security
3.12 How to Improve Cyber Security for Artificial Intelligence
3.13 Conclusion
References
4. Botnet Detection Using Artificial Intelligence
4.1 Introduction to Botnet
4.2 Botnet Detection
4.2.1 Host-Centred Detection (HCD)
4.2.2 Honey Nets-Based Detection (HNBD)
4.2.3 Network-Based Detection (NBD)
4.3 Botnet Architecture
4.3.1 Federal Model
4.3.1.1 IBN-Based Protocol
4.3.1.2 HTTP-Based Botnets
4.3.2 Devolved Model
4.3.3 Cross Model
4.4 Detection of Botnet
4.4.1 Perspective of Botnet Detection
4.4.2 Detection (Disclosure) Technique
4.4.3 Region of Tracing
4.5 Machine Learning
4.5.1 Machine Learning Characteristics
4.6 A Machine Learning Approach of Botnet Detection
4.7 Methods of Machine Learning Used in Botnet Exposure
4.7.1 Supervised (Administrated) Learning
4.7.1.1 Appearance of Supervised Learning
4.7.2 Unsupervised Learning
4.7.2.1 Role of Unsupervised Learning
4.8 Problems with Existing Botnet Detection Systems
4.9 Extensive Botnet Detection System (EBDS)
4.10 Conclusion
References
5. Spam Filtering Using AI
5.1 Introduction. 5.1.1 What is SPAM?
5.1.2 Purpose of Spamming
5.1.3 Spam Filters Inputs and Outputs
5.2 Content-Based Spam Filtering Techniques. 5.2.1 Previous Likeness–Based Filters
5.2.2 Case-Based Reasoning Filters
5.2.3 Ontology-Based E-Mail Filters
5.2.4 Machine-Learning Models
5.2.4.1 Supervised Learning
5.2.4.2 Unsupervised Learning
5.2.4.3 Reinforcement Learning
5.3 Machine Learning–Based Filtering. 5.3.1 Linear Classifiers
5.3.2 Naïve Bayes Filtering
5.3.3 Support Vector Machines
5.3.4 Neural Networks and Fuzzy Logics–Based Filtering
5.4 Performance Analysis
5.5 Conclusion
References
6. Artificial Intelligence in the Cyber Security Environment
6.1 Introduction
6.2 Digital Protection and Security Correspondences Arrangements
6.2.1 Operation Safety and Event Response
6.2.2 AI2
6.2.2.1 CylanceProtect
6.3 Black Tracking
6.3.1 Web Security
6.3.1.1 Amazon Macie
6.4 Spark Cognition Deep Military
6.5 The Process of Detecting Threats
6.6 Vectra Cognito Networks
6.7 Conclusion
References
7. Privacy in Multi-Tenancy Frameworks Using AI
7.1 Introduction
7.2 Framework of Multi-Tenancy
7.3 Privacy and Security in Multi-Tenant Base System Using AI
7.4 Related Work
7.5 Conclusion
References
8. Biometric Facial Detection and Recognition Based on ILPB and SVM
8.1 Introduction
8.1.1 Biometric
8.1.2 Categories of Biometric
8.1.2.1 Advantages of Biometric
8.1.3 Significance and Scope
8.1.4 Biometric Face Recognition
8.1.5 Related Work
8.1.6 Main Contribution
8.1.7 Novelty Discussion
8.2 The Proposed Methodolgy. 8.2.1 Face Detection Using Haar Algorithm
8.2.2 Feature Extraction Using ILBP
8.2.3 Dataset
8.2.4 Classification Using SVM
8.3 Experimental Results
8.3.1 Face Detection
8.3.2 Feature Extraction
8.3.3 Recognize Face Image
8.4 Conclusion
References
9. Intelligent Robot for Automatic Detection of Defects in Pre-Stressed Multi-Strand Wires and Medical Gas Pipe Line System Using ANN and IoT
9.1 Introduction
9.2 Inspection System for Defect Detection
9.3 Defect Recognition Methodology
9.4 Health Care MGPS Inspection
9.5 Conclusion
References
10. Fuzzy Approach for Designing Security Framework
10.1 Introduction
10.2 Fuzzy Set
10.3 Planning for a Rule-Based Expert System for Cyber Security
10.3.1 Level 1: Defining Cyber Security Expert System Variables
10.3.2 Level 2: Information Gathering for Cyber Terrorism
10.3.3 Level 3: System Design
10.3.4 Level 4: Rule-Based Model
10.4 Digital Security. 10.4.1 Cyber-Threats
10.4.2 Cyber Fault
10.4.3 Different Types of Security Services
10.5 Improvement of Cyber Security System (Advance)
10.5.1 Structure
10.5.2 Cyber Terrorism for Information/Data Collection
10.6 Conclusions
References
11. Threat Analysis Using Data Mining Technique
11.1 Introduction
11.2 Related Work
11.3 Data Mining Methods in Favor of Cyber-Attack Detection
11.4 Process of Cyber-Attack Detection Based on Data Mining
11.5 Conclusion
References
12. Intrusion Detection Using Data Mining
12.1 Introduction
12.2 Essential Concept
12.2.1 Intrusion Detection System
12.2.2 Categorization of IDS
12.2.2.1 Web Intrusion Detection System (WIDS)
12.2.2.2 Host Intrusion Detection System (HIDS)
12.2.2.3 Custom-Based Intrusion Detection System (CIDS)
12.2.2.4 Application Protocol-Based Intrusion Detection System (APIDS)
12.2.2.5 Hybrid Intrusion Detection System
12.3 Detection Program
12.3.1 Misuse Detection
12.3.1.1 Expert System
12.3.1.2 Stamp Analysis
12.3.1.3 Data Mining
12.4 Decision Tree
12.4.1 Classification and Regression Tree (CART)
12.4.2 Iterative Dichotomise 3 (ID3)
12.4.3 C 4.5
12.5 Data Mining Model for Detecting the Attacks
12.5.1 Framework of the Technique
12.6 Conclusion
References
13. A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT
13.1 Introduction
13.2 Literature Survey
13.3 Experimental Framework
13.4 Healthcare Monitoring
13.5 Results and Discussion
13.6 Conclusion
References
14. Vision-Based Gesture Recognition: A Critical Review
14.1 Introduction
14.2 Issues in Vision-Based Gesture Recognition
14.2.1 Based on Gestures
14.2.2 Based on Performance
14.2.3 Based on Background
14.3 Step-by-Step Process in Vision-Based
14.3.1 Sensing
14.3.2 Preprocessing
14.3.3 Feature Extraction
14.4 Classification
14.5 Literature Review
14.6 Conclusion
References
15. SPAM Filtering Using Artificial Intelligence
15.1 Introduction
15.2 Architecture of Email Servers and Email Processing Stages. 15.2.1 Architecture - Email Spam Filtering
15.2.1.1 Spam Filter - Gmail
15.2.1.2 Mail Filter Spam - Yahoo
15.2.1.3 Email Spam Filter - Outlook
15.2.2 Email Spam Filtering - Process
15.2.2.1 Pre-Handling
15.2.2.2 Taxation
15.2.2.3 Election of Features
15.2.3 Freely Available Email Spam Collection
15.3 Execution Evaluation Measures
15.4 Classification - Machine Learning Technique for Email Spam
15.4.1 Flock Technique - Clustering
15.4.2 Naïve Bayes Classifier
15.4.3 Neural Network
15.4.4 Firefly Algorithm
15.4.5 Fuzzy Set Classifiers
15.4.6 Support Vector Machine
15.4.7 Decision Tree
15.4.7.1 NBTree Classifier
15.4.7.2 C4.5/J48 Decision Tree Algorithm
15.4.7.3 Logistic Version Tree Induction (LVT)
15.4.8 Ensemble Classifiers
15.4.9 Random Forests (RF)
15.5 Conclusion
References
About the Editors
Index
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