Cybercrime in Social Media: Theory and Solutions

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
List of Contributors
1 Fundamental Theories Behind the Detection of Fake News and Rumors
1.1 Introduction
1.1.1 Fake News Gain Public Trust, How?
1.2 Definitions
1.3 Fundamental Theories
1.3.1 News-Related Theories
1.3.2 User-Related Theories
1.4 Fake News Detection Mechanisms
1.4.1 Knowledge-Based Fake News Detection
1.4.1.1 (Knowledge-Based) Expert-Based Manual Fact-Checking
1.4.1.2 (Knowledge-Based) Crowd-Sourced Manual Fact-Checking
1.4.1.3 (Knowledge-Based) Automatic Fact-Checking
1.4.2 Style-Based Fake News Detection
1.4.2.1 How to Capture the Style of News in Terms of Features
1.4.2.2 Machine Learning Approaches for Detection
1.4.3 Propagation-Based Fake News Detection
1.4.4 Source-Based Fake News Detection
1.5 Fake News Datasets
1.6 Results and Discussion
1.7 Conclusion
References
2 Social Media and Cybercrime: A Sociodemographic Study of Awareness Level Among Indian Youth
2.1 Introduction
2.2 Review of Literature
2.3 Objectives of the Study
2.4 Hypotheses of the Study
2.5 Methodology
2.5.1 Primary Data and Sampling
2.5.2 Research Instrument and Scale Validation
2.5.3 Demographic Profile
2.6 Analysis and Interpretation
2.7 Result and Discussion
2.8 Contribution of the Study
2.9 Conclusion
References
3 Emotions Detection From Social Media Text Using Machine Learning
3.1 Introduction
3.1.1 Why Do We Need to Detect Emotions?
3.2 Background and Existing Work
3.2.1 Existing Works
3.2.2 Techniques to Detect the Emotions From Text
3.2.3 Motivation
3.3 Methodology
3.3.1 Data Collection
3.3.2 Preprocessing
3.4 Feature Extraction
3.5 Data Split for Training and Testing
3.6 Classifiers: Machine Learning Models
3.7 Results
3.7.1 Results With Support Vector Machine Classifier
3.7.2 Results With Decision Tree Classifier
3.7.3 Results With Random Forest Classifier
3.7.4 Results With Random Forest Classifier Having Tuned Hyperparameters
3.8 Conclusion
Note
References
4 Hope Speech Detection On Social Media Platforms
4.1 Introduction
4.2 Related Works
4.2.1 Fake News Detection
4.2.2 Hate Speech Detection
4.2.3 Hope Speech Detection
4.2.4 Cyberbullying and Fake Profile Detection
4.3 Data and Methods
4.3.1 Dataset Description
4.3.2 Revisiting the Dataset
4.3.3 Reasons to Relabel the Dataset
4.3.4 Criteria of Relabelling
4.3.5 Data Pre-Processing
4.3.6 Model Description
4.4 Results
4.5 Discussion
4.6 Conclusion and Future Work
References
5 A Multilingual Review of Hate Speech Detection in Social Media Content
5.1 Introduction
5.2 What Is Hate Speech?
5.3 Hate Speech Identification Approaches
5.3.1 Research On Hate Speech and Offensive Language Identification in the English Language
5.3.2 Research On Hate Speech and Offensive Language Identification in Other Language
5.3.3 Research On Hate Speech and Offensive Language Identification in Code-Mixed Language
5.4 Conclusion and Future Directions
References
6 Analysis and Detection of Cyberbullying in Reddit Discussions
6.1 Introduction
6.2 Related Works
6.3 Methodology
6.3.1 Dataset Collection, Labelling, and Preprocessing
6.4 Temporal and Graph Properties Analysis
6.4.1 Proposed Approach
6.5 Results and Discussions
6.6 Conclusion and Future Work
Notes
References
7 A Doubled-Edged Sword Called Cyberspace: Introducing the Concept of Cybercrime
7.1 Introduction
7.2 Review Methodology
7.3 Concept of Cybercrime
7.4 Classification of Cybercrime
7.5 The Impact of Cybercrime
7.6 Conclusion
Notes
References
8 Cybercrime By Minors
8.1 Introduction
8.2 Methodology
8.2.1 Computer as a Tool and Target
8.2.2 Types of Cybercriminals
8.2.2.1 The Age Group Category 6 to 18 Years Old
8.2.2.2 Organized Hackers
8.2.2.3 Professional Hackers Or the Ethical Hackers
8.2.2.4 Discontented Employees
8.2.3 Types of Cybercrimes
8.2.3.1 Unauthorized Access
8.2.3.2 Cyber Fraud/Online Fraud
8.2.3.3 Spoof Websites and Email Security Alerts
8.2.3.4 Virus Hoax Emails
8.2.3.5 Lottery Frauds
8.2.3.6 Pharming
8.2.3.7 Spoofing
8.2.3.8 Credit Card Fraud
8.2.3.9 Cyber Theft
8.2.3.10 Identity Theft
8.2.3.11 Theft of Internet Hours
8.2.3.12 Theft of Computer System (Hardware)
8.2.3.13 Cyber Terrorism
8.2.4 Flowing of Virus, Trojan Horse, Worm, and Logical Bombs
8.2.4.1 Cyber Pornography
8.2.4.2 Online Grooming
8.2.4.3 Sexting
8.2.4.4 Defamation
8.2.4.5 Cyberstalking
8.2.4.6 E-Mail and IRC-Related Crimes
8.2.4.7 Hacking and Cracking
8.2.5 Hackers
8.2.5.1 White Hat Hackers
8.2.5.2 Black Hat Hackers
8.2.5.3 Gray Hat Hackers
8.2.5.4 Red Hat Hackers
8.2.5.5 Blue Hat Hackers
8.2.5.6 Green Hat Hackers
8.2.5.7 Script Kiddies
8.2.5.8 Whistle-Blowers
8.2.5.9 Hacktivist
8.2.5.10 State/Nation-Sponsored Hackers
8.2.5.11 Cyber Delinquency
8.2.6 More Youth Involvement in Cybercrimes
8.2.7 Causes of Cybercrimes Among Youth
8.2.8 What Attracts Teenagers Toward Internet?
8.2.9 Reforms to Prevent Juvenile Cyber Deliquency
8.3 Cybercrime Against Minors
8.3.1 Steps to Combat
8.4 Case Studies
8.4.1 Miami-Dade Public School System Cyberattacks
8.4.2 Madrid Teen Hacker Allegedly Perpetrates “Numerous” Hacks Since Late 2019
8.5 Results and Discussion
8.6 Conclusion
Notes
9 Deep Learning for Hate Speech and Offensive Language Detection
9.1 Introduction
9.2 Hate Speech and Offensive Language
9.2.1 Increasing Hate Speech and Offensive Language in Recent Times
9.2.2 Hate Speech as a Crime and Provisions in India
9.3 Detection Systems for HSOL
9.4 Deep Learning Detection Methods
9.4.1 Neural Network Architectures
9.4.2 Types of Deep Learning Methods
9.4.2.1 Word Embeddings-Based Methods
9.4.2.2 Transformer-Based Methods
9.4.3 Embeddings
9.4.3.1 Prediction-Based Embeddings (Word2vec)
9.4.3.2 Count-Based Embeddings (GloVe)
9.4.3.3 Bidirectional Encoder Representations From Transformers (BERT)
9.4.4 Classifiers
9.4.4.1 Naïve Bayes
9.4.4.2 Support Vector Machine (SVM)
9.4.4.3 Logistic Regression
9.4.4.4 Random Forest
9.4.4.5 Perceptron
9.4.5 Datasets Used
9.4.5.1 HASOC Dataset
9.4.5.2 Davidson HSOL Dataset
9.4.5.3 Founta Dataset
9.4.5.4 OLID Dataset
9.4.5.5 Sentiment140 Dataset
9.4.5.6 Gilbert Dataset
9.4.5.7 Tahmasbi and Rastegari Dataset
9.4.5.8 TREC 2020 (English)
9.4.5.9 Bohra Dataset
9.4.5.10 TREC 2020 (Hindi and Bangla)
9.5 Procedure of HSOL Detection
9.5.1 Convolutional Neural Networks (CNNs)
9.5.2 Recurrent Neural Networks (RNNs)
9.5.3 Gated Recurrent Unit (GRU)
9.5.4 CNN + GRU Combined Deep Neural Network
9.5.5 Long Short-Term Memory (LSTM) Model
9.5.6 Bi-Directional Long Short-Term Memory (Bi-LSTM) Model
9.5.7 Deep Convolutional Neural Network (DCNN) Model
9.6 Challenges Associated With Hate Speech Detection
9.6.1 Conflating Hate Speech With Offensive Language
9.6.2 Implicit Biases in Data-Driven Systems
9.6.3 Biases in Datasets and Embeddings
9.6.4 Availability and Inadequacy of Datasets
9.6.5 Rise of Adversarial Content
9.6.6 Potential Sources of Privacy Violations
9.6.7 Legal and Ideological Challenges
9.6.8 Code-Mixed Language
9.7 Conclusion
Notes
10 Speech Processing and Analysis for Forensics and Cybercrime: A Systematic Review
10.1 Introduction
10.2 Generic Model of Speech Processing and Analysis for Forensics
10.2.1 Speaker Recognition
10.2.2 Speech Enhancement
10.2.3 Speech Synthesis
10.3 Challenges
10.4 Motivation
10.5 Various Applications
10.5.1 Cyber Security in Social Media
10.5.1.1 Social Media Cyber Security Measures That Work
10.5.2 Military
10.5.2.1 Network Forensics
10.5.2.2 Image Forensics
10.5.2.3 Audio and Speech Forensics
10.5.3 Law Enforcement
10.5.3.1 Network Forensics
10.5.3.2 Image Forensics
10.5.3.3 Audio and Speech Forensics
10.5.4 Business Domains
10.5.4.1 Network Forensics
10.5.4.2 Image Forensics
10.5.4.3 Audio and Speech Forensics
10.6 Review in Detail
10.6.1 Pre-Processing
10.6.2 Feature Extraction
10.6.3 Analysis
10.6.3.1 Speaker Recognition
10.6.3.2 Speech Enhancement
10.6.3.3 Speech Synthesis
10.7 Datasets
10.7.1 Speaker Recognition Datasets
10.7.2 Speech Enhancement Datasets
10.7.3 Speech Synthesis Datasets
10.8 Discussion and Future Directions
10.9 Conclusion
References
11 Authentication Bypass Through Social Engineering
11.1 Introduction
11.1.1 Importance
11.2 Theoretical Background
11.2.1 Psychology of Social Engineering
11.2.2 Types of Social Engineering Attack
11.2.3 How to Prevent Social Engineering Attack
Algorithm
11.3 Proposed Methodology
11.3.1 Download the HTML Index of the Target Webpage
11.3.2 Creating a PHP File for Password Harvesting
11.3.3 Modify the Page HTML File to Incorporate Your PHP File in It
11.3.4 Hosting the PHP File for Password Storing
11.3.5 Hosting the Actual Phishing Page
11.4 Experiment and Implementation
11.5 Results and Discussion
11.5.1 Stage 1: Planning Phase
11.5.2 Stage 2: Attack Preparation
11.5.3 Stage 3: Attack Conducting Phase
11.5.4 Stage 4: Valuable Acquisition Phase
11.6 Conclusion
References
12 Emphasizing the Power of Natural Language Processing in Cyberbullying and Fake News Detection
12.1 Introduction
12.2 Related Works
12.3 Transformers With Attention Mechanism
12.4 Results and Discussion
12.5 Conclusion
References
Index