Artificial Intelligence and Data Mining Approaches in Security Frameworks: Advances and Challenges (Advances in Data Engineering and Machine Learning)

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
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
	2.6 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
		4.3.2 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
		4.7.2 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
	5.3 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
	6.3 Black Tracking
		6.3.1 Web Security
	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
		8.1.3 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
	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
	12.3 Detection Program
		12.3.1 Misuse Detection
	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
		15.2.2 Email Spam Filtering Process
		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
		15.4.8 Ensemble Classifiers
		15.4.9 Random Forests (RF)
	15.5 Conclusion
	References
About the Editors
Index
EULA