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E-raamat: Artificial Intelligence and Data Mining Approaches in Security Frameworks

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ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS Written 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 dened 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 xiii
1 Role of AI in Cyber Security
1(10)
Navani Siroya
Prof Manju Mandot
1.1 Introduction
2(1)
1.2 Need for Artificial Intelligence
2(1)
1.3 Artificial Intelligence in Cyber Security
3(2)
1.3.1 Multi-Layered Security System Design
3(1)
1.3.2 Traditional Security Approach and AI
4(1)
1.4 Related Work
5(1)
1.4.1 Literature Review
5(1)
1.4.2 Corollary
6(1)
1.5 Proposed Work
6(1)
1.5.1 System Architecture
7(1)
1.5.2 Future Scope
7(1)
1.6 Conclusion
7(4)
References
8(3)
2 Privacy Preserving Using Data Mining
11(22)
Chitra Jalota
Dr. Rashmi Agrawal
2.1 Introduction
11(3)
2.2 Data Mining Techniques and Their Role in Classification and Detection
14(5)
2.3 Clustering
19(2)
2.4 Privacy Preserving Data Mining (PPDM)
21(1)
2.5 Intrusion Detection Systems (IDS)
22(4)
2.5.1 Types of IDS
23(1)
2.5.1.1 Network-Based IDS
23(1)
2.5.1.2 Host-Based IDS
24(1)
2.5.1.3 Hybrid IDS
25(1)
2.6 Phishing Website Classification
26(1)
2.7 Attacks by Mitigating Code Injection
27(1)
2.7.1 Code Injection and Its Categories
27(1)
2.8 Conclusion
28(5)
References
29(4)
3 Role of Artificial Intelligence in Cyber Security and Security Framework
33(32)
Shweta Sharma
3.1 Introduction
34(2)
3.2 AI for Cyber Security
36(2)
3.3 Uses of Artificial Intelligence in Cyber Security
38(2)
3.4 The Role of AI in Cyber Security
40(6)
3.4.1 Simulated Intelligence Can Distinguish Digital Assaults
41(1)
3.4.2 Computer-Based Intelligence Can Forestall Digital Assaults
42(1)
3.4.3 Artificial Intelligence and Huge Scope Cyber Security
42(1)
3.4.4 Challenges and Promises of Artificial Intelligence in Cyber Security
43(1)
3.4.5 Present-Day Cyber Security and its Future with Simulated Intelligence
44(1)
3.4.6 Improved Cyber Security with Computer-Based Intelligence and AI (ML)
45(1)
3.4.7 AI Adopters Moving to Make a Move
45(1)
3.5 AI Impacts on Cyber Security
46(2)
3.6 The Positive Uses of AI Based for Cyber Security
48(1)
3.7 Drawbacks and Restrictions of Using Computerized Reasoning For Digital Security
49(1)
3.8 Solutions to Artificial Intelligence Confinements
50(1)
3.9 Security Threats of Artificial Intelligence
51(1)
3.10 Expanding Cyber Security Threats with Artificial Consciousness
52(3)
3.11 Artificial Intelligence in Cybersecurity -- Current Use-Cases and Capabilities
55(5)
3.11.1 AI for System Danger Distinguishing Proof
56(1)
3.11.2 The Common Fit for Artificial Consciousness in Cyber Security
56(1)
3.11.3 Artificial Intelligence for System Danger ID
57(1)
3.11.4 Artificial Intelligence Email Observing
58(1)
3.11.5 Simulated Intelligence for Battling Artificial Intelligence Dangers
58(1)
3.11.6 The Fate of Computer-Based Intelligence in Cyber Security
59(1)
3.12 How to Improve Cyber Security for Artificial Intelligence
60(1)
3.13 Conclusion
61(4)
References
62(3)
4 Botnet Detection Using Artificial Intelligence
65(22)
Astha Parihar
Neeraj Bhargava
4.1 Introduction to Botnet
66(1)
4.2 Botnet Detection
67(2)
4.2.1 Host-Centred Detection (HCD)
68(1)
4.2.2 Honey Nets-Based Detection (HNBD)
69(1)
4.2.3 Network-Based Detection (NBD)
69(1)
4.3 Botnet Architecture
69(4)
4.3.1 Federal Model
70(1)
4.3.1.1 IBN-Based Protocol
71(1)
4.3.1.2 HTTP-Based Botnets
71(1)
4.3.2 Devolved Model
71(1)
4.3.3 Cross Model
72(1)
4.4 Detection of Botnet
73(1)
4.4.1 Perspective of Botnet Detection
73(1)
4.4.2 Detection (Disclosure) Technique
73(1)
4.4.3 Region of Tracing
74(1)
4.5 Machine Learning
74(1)
4.5.1 Machine Learning Characteristics
74(1)
4.6 A Machine Learning Approach of Botnet Detection
75(1)
4.7 Methods of Machine Learning Used in Botnet Exposure
76(4)
4.7.1 Supervised (Administrated) Learning
76(1)
4.7.1.1 Appearance of Supervised Learning
77(1)
4.7.2 Unsupervised Learning
78(1)
4.7.2.1 Role of Unsupervised Learning
79(1)
4.8 Problems with Existing Botnet Detection Systems
80(1)
4.9 Extensive Botnet Detection System (EBDS)
81(2)
4.10 Conclusion
83(4)
References
84(3)
5 Spam Filtering Using AI
87(14)
Yojna Khandelwal
Dr. Ritu Bhargava
5.1 Introduction
87(2)
5.1.1 What is SPAM?
87(1)
5.1.2 Purpose of Spamming
88(1)
5.1.3 Spam Filters Inputs and Outputs
88(1)
5.2 Content-Based Spam Filtering Techniques
89(2)
5.2.1 Previous Likeness-Based Filters
89(1)
5.2.2 Case-Based Reasoning Filters
89(1)
5.2.3 Ontology-Based E-Mail Filters
90(1)
5.2.4 Machine-Learning Models
90(1)
5.2.4.1 Supervised Learning
90(1)
5.2.4.2 Unsupervised Learning
90(1)
5.2.4.3 Reinforcement Learning
91(1)
5.3 Machine Learning-Based Filtering
91(6)
5.3.1 Linear Classifiers
91(1)
5.3.2 Naive Bayes Filtering
92(2)
5.3.3 Support Vector Machines
94(1)
5.3.4 Neural Networks and Fuzzy Logics-Based Filtering
94(3)
5.4 Performance Analysis
97(1)
5.5 Conclusion
97(4)
References
98(3)
6 Artificial Intelligence in the Cyber Security Environment
101(18)
Jaya Fain
6.1 Introduction
102(2)
6.2 Digital Protection and Security Correspondences Arrangements
104(2)
6.2.1 Operation Safety and Event Response
105(1)
6.2.2 AI2
105(1)
6.2.2.1 Cylance Protect
105(1)
6.3 Black Tracking
106(4)
6.3.1 Web Security
107(1)
6.3.1.1 Amazon Macie
108(2)
6.4 Spark Cognition Deep Military
110(1)
6.5 The Process of Detecting Threats
111(1)
6.6 Vectra Cognito Networks
112(3)
6.7 Conclusion
115(4)
References
115(4)
7 Privacy in Multi-Tenancy Frameworks Using AI
119(10)
Shweta Solanki
7.1 Introduction
119(1)
7.2 Framework of Multi-Tenancy
120(2)
7.3 Privacy and Security in Multi-Tenant Base System Using AI
122(3)
7.4 Related Work
125(1)
7.5 Conclusion
125(4)
References
126(3)
8 Biometric Facial Detection and Recognition Based on ILPB and SVM
129(26)
Shubhi Srivastava
Ankit Kumar
Shiv Prakash
8.1 Introduction
129(10)
8.1.1 Biometric
131(1)
8.1.2 Categories of Biometric
131(1)
8.1.2.1 Advantages of Biometric
132(1)
8.1.3 Significance and Scope
132(1)
8.1.4 Biometric Face Recognition
132(4)
8.1.5 Related Work
136(1)
8.1.6 Main Contribution
136(1)
8.1.7 Novelty Discussion
137(2)
8.2 The Proposed Methodolgy
139(6)
8.2.1 Face Detection Using Haar Algorithm
139(2)
8.2.2 Feature Extraction Using ILBP
141(2)
8.2.3 Dataset
143(1)
8.2.4 Classification Using SVM
143(2)
8.3 Experimental Results
145(6)
8.3.1 Face Detection
146(1)
8.3.2 Feature Extraction
146(1)
8.3.3 Recognize Face Image
147(4)
8.4 Conclusion
151(4)
References
152(3)
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(18)
Rajesh Kanna
O. Pandithurai
N. Anand
P. Sethuramalingam
Abdul Munaf
9.1 Introduction
156(2)
9.2 Inspection System for Defect Detection
158(4)
9.3 Defect Recognition Methodology
162(3)
9.4 Health Care MGPS Inspection
165(3)
9.5 Conclusion
168(5)
References
169(4)
10 Fuzzy Approach for Designing Security Framework
173(24)
Kapil Chauhan
10.1 Introduction
173(4)
10.2 Fuzzy Set
177(8)
10.3 Planning for a Rule-Based Expert System for Cyber Security
185(3)
10.3.1 Level 1: Denning Cyber Security Expert System Variables
185(1)
10.3.2 Level 2: Information Gathering for Cyber Terrorism
185(1)
10.3.3 Level 3: System Design
186(1)
10.3.4 Level 4: Rule-Based Model
187(1)
10.4 Digital Security
188(2)
10.4.1 Cyber-Threats
188(1)
10.4.2 Cyber Fault
188(1)
10.4.3 Different Types of Security Services
189(1)
10.5 Improvement of Cyber Security System (Advance)
190(1)
10.5.1 Structure
190(1)
10.5.2 Cyber Terrorism for Information/Data Collection
191(1)
10.6 Conclusions
191(6)
References
192(5)
11 Threat Analysis Using Data Mining Technique
197(12)
Riddhi Panchal
Binod Kumar
11.1 Introduction
198(1)
11.2 Related Work
199(2)
11.3 Data Mining Methods in Favor of Cyber-Attack Detection
201(3)
11.4 Process of Cyber-Attack Detection Based on Data Mining
204(1)
11.5 Conclusion
205(4)
References
205(4)
12 Intrusion Detection Using Data Mining
209(20)
Astha Parihar
Pramod Singh Rathore
12.1 Introduction
209(1)
12.2 Essential Concept
210(6)
12.2.1 Intrusion Detection System
211(1)
12.2.2 Categorization of IDS
212(1)
12.2.2.1 Web Intrusion Detection System (WIDS)
213(1)
12.2.2.2 Host Intrusion Detection System (HIDS)
214(1)
12.2.2.3 Custom-Based Intrusion Detection System (CIDS)
215(1)
12.2.2.4 Application Protocol-Based Intrusion Detection System (APIDS)
215(1)
12.2.2.5 Hybrid Intrusion Detection System
216(1)
12.3 Detection Program
216(5)
12.3.1 Misuse Detection
217(1)
12.3.1.1 Expert System
217(1)
12.3.1.2 Stamp Analysis
218(2)
12.3.1.3 Data Mining
220(1)
12.4 Decision Tree
221(2)
12.4.1 Classification and Regression Tree (CART)
222(1)
12.4.2 Iterative Dichotomise 3 (ID3)
222(1)
12.4.3 C 4.5
223(1)
12.5 Data Mining Model for Detecting the Attacks
223(3)
12.5.1 Framework of the Technique
224(2)
12.6 Conclusion
226(3)
References
226(3)
13 A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT
229(18)
Rajesh Kanna
V. Nagaraju
D. Jayashree
Abdul Munaf
M. Ashok
13.1 Introduction
230(1)
13.2 Literature Survey
231(1)
13.3 Experimental Framework
232(5)
13.4 Healthcare Monitoring
237(3)
13.5 Results and Discussion
240(2)
13.6 Conclusion
242(5)
References
243(4)
14 Vision-Based Gesture Recognition: A Critical Review
247(14)
Neela Harish
Praveen
Prasanth
Aparna
Athaf
14.1 Introduction
247(1)
14.2 Issues in Vision-Based Gesture Recognition
248(1)
14.2.1 Based on Gestures
249(1)
14.2.2 Based on Performance
249(1)
14.2.3 Based on Background
249(1)
14.3 Step-by-Step Process in Vision-Based
249(4)
14.3.1 Sensing
251(1)
14.3.2 Preprocessing
252(1)
14.3.3 Feature Extraction
252(1)
14.4 Classification
253(1)
14.5 Literature Review
254(4)
14.6 Conclusion
258(3)
References
258(3)
15 SPAM Filtering Using Artificial Intelligence
261(34)
Abha Jain
15.1 Introduction
261(4)
15.2 Architecture of Email Servers and Email Processing Stages
265(4)
15.2.1 Architecture -- Email Spam Filtering
265(1)
15.2.1.1 Spam Filter -- Gmail
266(1)
15.2.1.2 Mail Filter Spam -- Yahoo
266(1)
15.2.1.3 Email Spam Filter -- Outlook
267(1)
15.2.2 Email Spam Filtering -- Process
267(1)
15.2.2.1 Pre-Handling
268(1)
15.2.2.2 Taxation
268(1)
15.2.2.3 Election of Features
268(1)
15.2.3 Freely Available Email Spam Collection
269(1)
15.3 Execution Evaluation Measures
269(6)
15.4 Classification -- Machine Learning Technique for Email Spam
275(15)
15.4.1 Flock Technique -- Clustering
275(1)
15.4.2 Naive Bayes Classifier
276(3)
15.4.3 Neural Network
279(3)
15.4.4 Firefly Algorithm
282(1)
15.4.5 Fuzzy Set Classifiers
283(1)
15.4.6 Support Vector Machine
284(2)
15.4.7 Decision Tree
286(1)
15.4.7.1 NBTree Classifier
286(1)
15.4.7.2 C4.5/J48 Decision Tree Algorithm
287(1)
15.4.7.3 Logistic Version Tree Induction (LVT)
287(1)
15.4.8 Ensemble Classifiers
288(1)
15.4.9 Random Forests (RF)
289(1)
15.5 Conclusion
290(5)
References
290(5)
Index 295
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.

Ritu Bhargava, PhD, is an assistant professor in the Department of Computer Science at Sophia Girls College in Ajmer, India, having earned her PhD in computer science from Hemchandracharya North Gujarat University Patan, Gujarat, India. She has more than 15 years of active teaching and research experience and has contributed to three books and more than 30 papers in scientific and technical journals. She has also been an organizing chair on over 15 scientific conferences, and, like her colleague, her work on face recognition and fingerprint recognition is well-known and often cited.

Pramod Singh Rathore, MTech, is an assistant professor at the Aryabhatta College of Engineering and Research Center and visiting faculty member at MDSU in Ajmer, India. He is a PhD in computer science and engineering at the University of Engineering and Management and already has eight years of teaching experience and over 45 papers in scientific and technical journals. He has also co-authored and edited numerous books.

Rashmi Agrawal, PhD, is a professor in the Department of Computer Applications at the Manav Rachna International Institude of Research and Studies in Faridabad, India with more than 18 years of teaching experience. She is a book series editor and the associate editor on a scientific journal on data science and the internet of things. She has published many research papers in scientific and technical journals in these areas and contributed multiple chapters to numerous books. She is currently guiding PhD students and is an active reviewer and editorial board member of various journals.
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