Machine learning for authorship attribution and cyber forensics
In: International series on computer entertainment and media technology
Intro -- Acknowledgments -- Contents -- Chapter 1: Cybersecurity And Cybercrime Investigation -- 1.1 Cybersecurity -- 1.2 Key Components to Minimizing Cybercrimes -- 1.3 Damage Resulting from Cybercrime -- 1.4 Cybercrimes -- 1.4.1 Major Categories of Cybercrime -- 1.4.2 Causes of and Motivations for Cybercrime -- 1.5 Major Challenges -- 1.5.1 Hacker Tools and Exploit Kits -- 1.5.2 Universal Access -- 1.5.3 Online Anonymity -- 1.5.4 Organized Crime -- 1.5.5 Nation State Threat Actors -- 1.6 Cybercrime Investigation -- Chapter 2: Messaging Forensics In Perspective -- 2.1 Sources of Cybercrimes -- 2.2 Sample Analysis Tools and Techniques in Literature -- 2.3 Proposed Framework for Cybercrimes Investigation -- 2.4 Authorship Analysis -- 2.5 Introduction to Criminal Information Mining -- 2.5.1 Existing Criminal Information Mining Approaches -- 2.5.2 WordNet-Based Criminal Information Mining -- 2.6 WEKA -- Chapter 3: Analyzing Network Level Information -- 3.1 Statistical Evaluation -- 3.2 Temporal Analysis -- 3.3 Geographical Localization -- 3.4 Social Network Analysis -- 3.5 Classification -- 3.6 Clustering -- Chapter 4: Authorship Analysis Approaches -- 4.1 Historical Perspective -- 4.2 Online Anonymity and Authorship Analysis -- 4.3 Stylometric Features -- 4.4 Authorship Analysis Methods -- 4.4.1 Statistical Analysis Methods -- 4.4.2 Machine Learning Methods -- 4.4.3 Classification Method Fundamentals -- 4.5 Authorship Attribution -- 4.6 Authorship Characterization -- 4.7 Authorship Verification -- 4.8 Limitations of Existing Authorship Techniques -- Chapter 5: Writeprint Mining For Authorship Attribution -- 5.1 Authorship Attribution Problem -- 5.1.1 Attribution Without Stylistic Variation -- 5.1.2 Attribution with Stylistic Variation -- 5.2 Building Blocks of the Proposed Approach -- 5.3 Writeprint -- 5.4 Proposed Approaches.