Securing Social Networks in Cyberspace
- Length: 316 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-10-19
- ISBN-10: 0367681730
- ISBN-13: 9780367681739
- Sales Rank: #0 (See Top 100 Books)
This book collates the key security and privacy concerns faced by individuals and organizations who use various social networking sites. This includes activities such as connecting with friends, colleagues, and family; sharing and posting information; managing audio, video, and photos; and all other aspects of using social media sites both professionally and personally. In the setting of the Internet of Things (IoT) that can connect millions of devices at any one time, the security of such actions is paramount. Securing Social Networks in Cyberspace discusses user privacy and trust, location privacy, protecting children, managing multimedia content, cyberbullying, and much more. Current state-of-the-art defense mechanisms that can bring long-term solutions to tackling these threats are considered in the book.
This book can be used as a reference for an easy understanding of complex cybersecurity issues in social networking platforms and services. It is beneficial for academicians and graduate-level researchers. General readers may find it beneficial in protecting their social-media-related profiles.
Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface Acknowledgment Editor Contributors Part I: Protection of Personal Information in Social Networks Chapter 1: User Awareness for Securing Social Networks 1.1 Introduction 1.2 Evolution of Social Networking Websites 1.2.1 History 1.3 Emergence of Social Networking Sites 1.4 The Dark Side of Social Networking 1.5 Findings and Ways to Raise User Awareness 1.6 Discussion References Chapter 2: Privacy-Preserving Analytics for Social Network Data: A Survey of Currently Prevalent Tools 2.1 Introduction 2.2 Social Media Privacy Threats 2.3 A Graph-Based Approach to Modeling Privacy Breaches 2.3.1 Identity Disclosure Attack 2.3.2 Attribute Disclosure Attack 2.4 Privacy-Preserving Analytics Techniques 2.4.1 K-Anonymity 2.4.2 L-Diversity 2.4.3 T-Closeness 2.4.4 Differential Privacy 2.5 Tools for Privacy-Preserving Analytics 2.5.1 ARX 2.5.2 TIAMAT 2.5.3 SECRETA 2.5.3.1 SECRETA Frontend 2.5.3.2 SECRETA Backend 2.5.4 Amnesia 2.5.5 IBM Differential Privacy Library 2.5.6 OpenDP 2.6 Directions for Future Research 2.6.1 Privacy of Text Data 2.6.2 Privacy of Social Media Profile Attributes 2.6.3 Privacy of Time and Location-Tagged Information 2.6.4 Privacy of Heterogeneous Information 2.6.5 Prevention of Identity and Attribute Disclosure Attacks 2.6.6 Understanding and Safeguarding Privacy of Special Needs Groups 2.6.7 Identifying New Techniques to Preserve Privacy 2.7 Conclusion References Chapter 3: Enabling Location k-Anonymity in Social Networks 3.1 Introduction 3.2 Related Work 3.3 Motivations and Contribution of This Work 3.4 The Proposed Protocol 3.4.1 Actors and Territory Setting 3.4.2 The LTS Hierarchy 3.4.3 Circle Operation 3.4.4 Position Notification 3.4.5 LBS Request 3.4.6 LBS Response 3.5 Security Analysis 3.6 Future Research Directions and Challenges 3.7 Conclusion Acknowledgment References Part II: Securing Multimedia Contents Chapter 4: Automated Content Classification in Social Media Platforms 4.1 Introduction 4.1.1 Types of Content 4.1.2 Automation 4.2 Laws and Regulations 4.3 Community Standards 4.3.1 YouTube 4.3.2 Facebook 4.3.3 Twitter 4.3.4 Twitch 4.3.5 Parler 4.3.6 Summary 4.4 Automated Content Classification 4.4.1 Machine Learning 4.4.1.1 Quantity and Quality of Training Data 4.4.1.2 Identification of Important Features 4.4.1.3 Choice of Machine Learning Algorithm 4.4.1.4 Validation Metrics 4.4.2 Deep Learning 4.4.3 Current Efforts by Social Media Platforms 4.5 Challenges in Automated Content Classification 4.5.1 Subjectivity and Context 4.5.2 Bias 4.5.3 Livestreaming 4.5.4 Adversarial 4.6 Information Sharing Between Platforms 4.7 Conclusions References Chapter 5: Steganographic Botnet C&C Channel Using Twitter 5.1 Introduction 5.1.1 Motivation 5.1.2 Statement of Problem 5.1.3 Research Goals 5.1.4 Structure of Chapter 5.2 Literature Review 5.2.1 Steganography and Steganalysis 5.2.2 Basic Steganalysis 5.2.3 Steganography Paradigms 5.2.4 Batch Steganography 5.2.5 Key Exchange in Steganography 5.2.6 Botnets 5.2.7 Botnet C&C 5.2.8 Botnets Using Smartphones 5.2.9 Related Works 5.2.10 Huffman Codes 5.2.11 Binary Huffman Coding 5.2.12 Natural Language Processing 5.2.13 Markov Chains 5.2.14 Laplace Smoothing 5.3 Methodology 5.3.1 Twitter Covert Channel 5.3.1.1 The Stego System 5.3.1.2 The Tweet Generator 5.3.1.3 Key Exchange 5.3.1.4 Posting to Twitter 5.3.2 The Botnet C&C Language 5.3.2.1 Using Huffman Codes to Improve Message Transmission 5.3.3 Username Generation Using Markov Chains 5.3.4 Data Collection 5.4 Evaluation of Results 5.4.1 Username Generation Analysis 5.4.1.1 Scoring Names Based on the Generated Markov Chain 5.4.2 Scoring Names Using Human Analysis with Mechanical Turk 5.4.3 Efficacy of Mechanical Turk 5.4.4 The Mechanical Turk Experiment 5.4.5 Huffman Coding Compression Rate 5.4.6 Stego System Evaluation 5.4.6.1 Emulab Performance and Reliability Experiment 5.4.7 Capacity 5.4.8 Steganographic Security 5.4.9 Robustness 5.5 Conclusion 5.5.1 Summary 5.5.2 Future Work 5.5.2.1 Improving the Bandwidth of the System 5.5.2.2 Improving the Generated Tweets 5.5.2.3 Improvements and Future Applications References Chapter 6: A Deep Learning-Based Model for an Efficient Hate-Speech Detection in Twitter 6.1 Introduction 6.2 Related Works 6.3 Proposed Approach 6.3.1 Dataset Used 6.3.2 Proposed Model Description 6.3.2.1 Tokenization 6.3.2.2 Conversion to Lower Case 6.3.2.3 Removing Twitter Handle, Stop Words, Special Characters, Punctuation and Numbers 6.3.2.4 N-Grams 6.3.2.5 Target Label Encoding 6.3.2.6 Embedding 6.3.2.7 Attention-Based DNN 6.3.2.8 CNN-LSTM Based DNN 6.4 Experimental Results and Discussion 6.4.1 Performance Metrics 6.4.2 Experimental Settings and Results 6.5 Result Discussion 6.5.1 Hate-Offensive Classification 6.5.2 Hate-Neutral Classification 6.5.3 Offensive-Neutral Classification 6.5.4 Hate-Offensive-Neutral Classification 6.6 Comparative Study 6.7 Error Analysis 6.8 Future Research Directions 6.9 Conclusion References Part III: Cyberbullying, Cyberstalking, and Related Issues Chapter 7: Cyberbullying and Cyberstalking on Online Social Networks 7.1 Introduction 7.2 Cyberbullying Categories 7.3 The Consequences of Cyberbullying 7.4 Cyberstalking 7.5 Cyberbullying on Online Social Networks 7.6 Cyberbullying Detection Steps 7.6.1 Data Preprocessing Steps 7.6.2 Features Used for Cyberbullying Detection 7.6.3 Cyberbullying Detection Methods 7.6.3.1 Supervised Methods 7.6.3.2 Semi-Supervised Methods 7.6.3.3 Lexicon-Based Methods 7.6.3.4 Rule-Based Methods 7.7 Success Metrics Used in Cyberbullying Studies 7.8 Future Direction of Cyberbullying 7.9 Conclusion References Chapter 8: Cyberbullying Severity Detection Using Deep Learning Techniques: A Multi-Class Classification over Varied Class Balance Data 8.1 Background 8.2 Problem Statement 8.3 Motivation 8.4 Paper Organization 8.5 Related Work 8.5.1 Cyberbullying Detection: Binary Classification 8.5.2 S everity Detection: Multi-Class Classification 8.6 Contributions 8.7 Experimental Setup 8.7.1 Dataset 8.7.2 Data Pre-Processing 8.7.3 Proposed Deep Learning Techniques 8.7.3.1 CNN 8.7.3.2 HLSTM – HAN & BiLSTM 8.7.3.3 RNN 8.8 Experiments 8.9 Evaluation Techniques 8.10 Results 8.11 Discussion 8.12 Conclusion and Future Work References Chapter 9: Cyberbullying among Neurodiverse Learners during Online Teaching and Learning amidst COVID-19 9.1 Background 9.2 Bullying 9.3 Children with Special Needs 9.3.1 Physical Disability 9.3.2 Developmental Disability 9.3.3 Emotional/Behavioral Disability 9.3.4 Sensory Impairment 9.4 Bullying in Children with Special Needs 9.5 Cyberbullying 9.6 Cyberbullying in Children with Special Needs 9.7 Related Work 9.8 Methods 9.8.1 Procedure 9.8.2 Participants 9.8.3 Data Analysis 9.9 Results 9.9.1 The Pandemic Effect 9.9.2 Bullying/Cyberbullying Perception 9.9.3 Bullying/Cyberbullying Experiences 9.9.4 Bullying/Cyberbullying Impact 9.9.5 Online Teaching and Learning during the Pandemic 9.9.6 Cyberbullying during the Pandemic 9.9.7 Bullying/Cyberbullying Intervention 9.9.8 Parents’ Recommendations 9.10 Discussion 9.11 Conclusion References Part IV: Other Issues for Securing Social Networks and Online Profiles Chapter 10: Profiling Online Users: Emerging Approaches and Challenges 10.1 Introduction 10.2 Sources of Online User Profile Data 10.3 Modeling of User Online Behavior 10.3.1 Rule-Based Models 10.3.2 Machine Learning Models 10.3.2.1 Supervised Learning 10.3.2.2 Unsupervised Learning 10.3.3 Data Collection 10.3.3.1 Pre-ProcessingData 10.3.3.2 Network Sniffing 10.3.3.3 Host Data 10.3.3.4 Publicly Available Datasets 10.3.3.5 Feature Extraction 10.3.4 User Profiling Based on Web-Based Fingerprinting 10.3.5 Creating a User Profile 10.3.5.1 Case Study: An Architecture for Generating a Labeled Private Traffic Dataset 10.4 User Online Interest Modeling 10.4.1 Data Collection and Processing 10.4.2 Data Collecting Architecture 10.4.3 Data Source and Feature Selection 10.4.4 Machine Learning Models 10.4.5 Natural Language Processing 10.4.6 Profiling Groups of Users 10.5 Privacy Issues 10.6 Future Work 10.7 Conclusion References Chapter 11: Securing Mobile Social Networks 11.1 Introduction 11.2 Mobile Security 11.3 Being Secure Means Remaining Skeptical 11.4 Social Networking Users Are Being Targeted for Cybercrime 11.5 Mobile Device Risks and Social Media Risks 11.5.1 Social Engineering Risk 11.5.1.1 Security Options for Mobile Social Networking 11.5.1.2 Cellular Protection Services 11.5.2 Cellular Protection Programs 11.5.2.1 Security Protocol Risk 11.5.3 Other Cyber Threats 11.5.3.1 Mobile Ransomware 11.5.3.2 Botnets 11.5.3.3 Malicious Apps 11.5.3.4 Password Risk 11.6 The Future Risks Associated with Social Networking on Mobile Devices 11.6.1 Mobile Devices and Privacy 11.6.2 Electronic Evidence in Court 11.6.3 Crimes and Incidents Involved with Electronic Forensic Investigations 11.6.4 US Patriot Act of 11.6.5 The Electronic Communication Privacy Act (ECPA) 11.6.6 Security and Computer Policy Use 11.6.7 Types of Electronic Evidence 11.6.8 Internet Logs 11.6.9 Network Logs 11.6.10 Data Logs 11.7 Examining the Future 11.8 Conclusion References Chapter 12: Protecting Regular and Social Network Users in a Wireless Network by Detecting Rogue Access Point: Limitations and Countermeasures 12.1 Introduction 12.2 Rogue Access Point (RAP) 12.3 Rogue Access Point Detection Approaches 12.3.1 Traditional Approach 12.3.2 Client-Side Approach 12.3.2.1 Round Trip Time 12.3.2.2 Received Signal Strength and Sequential Hypothesis Testing 12.3.3 Server-Side Approach 12.3.3.1 Using Temporal Characteristics 12.3.3.2 Hidden Markov Model 12.3.3.3 Clock Skew 12.3.3.4 Hybrid Framework 12.3.4 Hybrid Approach 12.3.4.1 Multi-Agent Sourcing 12.3.4.2 Covert Channel 12.4 Existing Methods for RAP Detection and Their Limitations 12.5 The Parameters 12.5.1 SSID 12.5.2 MAC Address 12.5.3 Wi-Fi Scanner 12.5.4 Beacon Frames 12.5.5 Channel and Frequency 12.5.6 RSSI (Received Signal Strength Indicator) 12.5.7 Authentication Type 12.5.8 Radio Type 12.6 System Architecture 12.7 Experimental Setting 12.7.1 The Setting 12.7.2 Discussion on the Results 12.8 Some Countermeasures for RAP 12.8.1 Implement Physical Security Controls 12.8.2 Identify Authorized Devices 12.8.3 Build and Use Network Access Control 12.8.4 Actively Monitor Network-Connected Devices 12.8.5 Enable the Web Firewall 12.8.6 Advanced Switch Port Monitoring Tool 12.8.7 Corporate Policy and User Awareness 12.8.8 Mutual Authentication 12.8.9 Sniffers and WIDS 12.9 Conclusions References Chapter 13: A Tutorial on Cross-Site Scripting Attack: Defense against Online Social Networks 13.1 Introduction 13.2 What Is XXS 13.3 Types of Attack 13.3.1 Stored XSS Attacks 13.3.2 Reflected XSS Attacks 13.3.3 DOM-Based XSS 13.4 Tutorial on JavaScript Attack 13.4.1 How the Attack Works 13.4.2 Mitigation of the Attack 13.5 Tutorial on SQL Injection Attack 13.5.1 How the Attack Works 13.5.2 Mitigation of the Attack 13.6 Discussion: Conclusions References Index
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.