Anomaly Detection: Techniques and Applications

✍ Scribed by Saira Banu, Shriram Raghunathan, Dinesh Mavaluru, A. Syed Mustafa

• Publisher: Nova Science
• Year: 2021
• Tongue: English
• Leaves: 190
• Series: Computer Science, Technology and Applications
• Category: Library

✦ Table of Contents

Contents
Preface
Acknowledgment
Chapter 1
Secured and Automated Key Establishment and Data Forwarding Scheme for the Internet of Things
Abstract
1.1. Introduction
1.2. Related Works
1.3. Secured and Automated Key Establishment Management
1.4. Network Model
1.5. Optimal Proxy Server Selection Using Modified Bat Algorithm
1.5.1. Modified Bat Algorithm
1.6. Colloborative Key Handling Management
1.7. Key Handling Procedure
1.8. Experimental Results
Conclusion
References
Chapter 2
A Study of Enhanced Anomaly Detection Techniques Using Evolutionary-Based Optimization for Improved Detection Accuracy
Abstract
2.1. Introduction
2.2. Literature Analysis
2.3. Classifier-Based Approaches
2.4. Evolutionary-Based Approaches
Conclusion
References
Chapter 3
Anomaly Detection and Applications
Abstract
3.1. Introduction of Outlier Detection
3.2. Application with Musk Dataset
3.3. Linear Models Introduction
3.4. Introduction of Principal Component Analysis (PCA) in Outlier Detection
3.5. Application with Vowels Dataset
3.6. Introduction of One Class Support Vector Machines (SVM)
3.7. Application with RapidMiner
3.8. Neural Networks
3.9. Limitation of Linear Models
3.10. Proximity-Based Methods for Outlier Detection
3.10.1. Introduction of Cluster-Based Model in Outlier Detection
3.10.2. Clustering-based Multivariate Gaussian Outlier Score (CMGOS)
3.10.3. Application with Vowels Dataset
3.10.4. Distance-Based Methods for Anomaly Detection
3.10.5. Average k-Nearest Neighbor Score for Outlier Detection
3.10.6. Application with Vowels
3.11. Density-Based Methods for Anomaly Detection
3.11.1. Local Outlier Factor (LOF)
3.11.2. Application with Wisconsin-Breast Cancer Diagnostics Dataset
3.11.3. Limitation of Proximity-Based Models
3.12. High-Dimensional Outlier Detection
3.12.1. Introduction of High-Dimensional Outlier detection
3.12.2. Isolation Forest (IForest)
3.12.3. Application with Wisconsin-Breast Cancer Diagnostics Dataset
3.12.4. Limitation of High Dimensional Method
3.13. Introduction of Integrated Models
3.13.1. Concrete Case
Conclusion
References
Chapter 4
An Evolutionary Study on SIoT (Social Internet of Things)
Abstract
4.1. Introduction
4.2. IoT Architecture and Threats
A. Perception Layer
B. Transport Layer
C. Application Layer
4.3. Taxonomy of Trust in IoT
A. Properties of Trust
B. Trust Management
4.4. IoT Security
A. Fundamental Aims of Security: CIA Model
B. Conventional Security in IT versus IoT Security
4.5. Issues and Security Solutions for IoT Communication Protocols
A. Protocols, Issues, and Solution in SIoT
B. General Security Issues in IoT
4.6. Challenges in IoT - Near Future
4.6.1. Lack of Security
4.6.2. Lack of Privacy
4.6.3. Storage Issues
4.6.4. E-Waste
4.6.5. Energy Demands
4.7. Conclusion
References
Chapter 5
A Critical Study on Advanced Machine Learning Classification of Human Emotional State Recognition Using Facial Expressions
Abstract
5.1. Introduction
5.1.1. Pre-Processing
5.1.2. Feature Extraction
5.1.3. Classification
5.1.4. Facial Expression Databases
5.1.5. Deep Learning
5.2. Literature Survey
5.2.1. Survey about FER
5.2.2. Survey on Classifiers Based FER
5.2.3. Survey on Feature-Based FER
5.2.4. Survey on Deep Learning Based FER
5.3. Discussions
5.4. Conclusion
Conclusion
Funding
Competing Interests
Acknowledgment
References
Chapter 6
Anomaly Detection for Data Aggregation in Wireless Sensor Networks
Abstract
1. Introduction
2. Existing System
3. Proposed System
4. Methodology
Boneh’s Scheme
Sensor Node Creation
Cluster Header
Base Station Activation
Aggregation and Temporal Key Generation and Signature Generation
Verify and Integrity of the Data
5. Result and Analysis
Conclusion
References
Chapter 7
Algorithm for Real Time Anomalous User Detection from Call Detail Record
Abstract
1. Introduction
2. Features of a Spam Caller
3. Survey on Spam Detection Approaches
4. Trust Value Calculation
Dataset
Conclusion
References
Chapter 8
Secured Transactions from
the Anomaly User Using 2 Way SSL
Abstract
1. Introduction
2. SSL Certifcates to Prevent from Anomaly Users
3. REST API/ Microservice
Encryption Techniques Used in Industry
Asymmetric Encryption
Public Key
Private Key
Export the Private Key from Pkcs 12 Format Keystore
Export the Public Certificate from Pkcs12 Format Keystore
What Is SSL and How SSL Handshake Works
4. IAM (Identity and Access Management
Systems) Authentication
5. Basic Authentication
6. Oauth
Reference
About the Editors
Index
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