Machine Learning for Authorship Attribution and Cyber Forensics, Iqbal Farkhund, Debbabi Mourad, Fung Benjamin C. M.
Автор: Iqbal Farkhund, Debbabi Mourad, Fung Benjamin C. M. Название: Machine Learning for Authorship Attribution and Cyber Forensics ISBN: 3030616746 ISBN-13(EAN): 9783030616748 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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 291.5.3 Online Anonymity 1.5.4 Organized Crime 301.5.5 Nation State Threat Actors 311.6 CYBERCRIME INVESTIGATION 322 MACHINE LEARNING FRAMEWORK FOR MESSAGING FORENSICS 342.1 SOURCES OF CYBERCRIMES 362.2 FEW ANALYSIS TOOLS AND TECHNIQUES 382.3 PROPOSED FRAMEWORK FOR CYBERCRIMES INVESTIGATION 392.4 AUTHORSHIP ANALYSIS 412.5 INTRODUCTION TO CRIMINAL INFORMATION MINING 432.5.1 Existing Criminal Information Mining Approaches 442.5.2 WordNet-based Criminal Information Mining 472.6 WEKA 483 HEADER-LEVEL INVESTIGATION AND ANALYZING NETWORK INFORMATION 503.1 STATISTICAL EVALUATION 523.2 TEMPORAL ANALYSIS 533.3 GEOGRAPHICAL LOCALIZATION 533.4 SOCIAL NETWORK ANALYSIS 553.5 CLASSIFICATION 563.6 CLUSTERING 584 AUTHORSHIP ANALYSIS APPROACHES 594.1 HISTORICAL PERSPECTIVE 594.2 ONLINE ANONYMITY AND AUTHORSHIP ANALYSIS 604.3 STYLOMETRIC FEATURES 614.4 AUTHORSHIP ANALYSIS METHODS 634.4.1 Statistical Analysis Methods 644.4.2 Machine Learning Methods 644.4.1 Classification Method Fundamentals 664.5 AUTHORSHIP ATTRIBUTION 674.6 AUTHORSHIP CHARACTERIZATION 694.7 AUTHORSHIP VERIFICATION 704.8 LIMITATIONS OF EXISTING AUTHORSHIP TECHNIQUES 725 AUTHORSHIP ANALYSIS - WRITEPRINT MINING FOR AUTHORSHIP ATTRIBUTION 745.1 AUTHORSHIP ATTRIBUTION PROBLEM 785.1.1 Attribution without Stylistic Variation 795.1.2 Attribution with Stylistic Variation 795.2 BUILDING BLOCKS OF THE PROPOSED APPROACH 805.3 WRITEPRINT 875.4 PROPOSED APPROACHES 875.4.1 AuthorMiner1: Attribution without Stylistic Variation 885.4.2 AuthorMiner2: Attribution with Stylistic Variation 926 AUTHORSHIP ATTRIBUTION WITH FEW TRAINING SAMPLES 976.1 PROBLEM STATEMENT AND FUNDAMENTALS 1006.2 PROPOSED APPROACH 1016.2.1 Preprocessing 1016.2.2 Clustering by Stylometric Features 1026.2.3 Frequent Stylometric Pattern Mining 1046.2.4 Writeprint Mining 1056.2.5 Identifying Author 1066.3 EXPERIMENTS AND DISCUSSION 1067 AUTHORSHIP CHARACTERIZATION 1137.1 PROPOSED APPROACH 1157.1.1 Clustering Anonymous Messages 1167.1.2 Extracting Writeprints from Sample Messages 1167.1.3 Identifying Author Characteristics 1167.2 EXPERIMENTS AND DISCUSSION 1178 AUTHORSHIP VERIFICATION 1208.1 PROBLEM STATEMENT 1238.2 PROPOSED APPROACH 1258.2.1 Verification by Classification 1268.2.2 Verification by Regression 1268.3 EXPERIMENTS AND DISCUSSION 1278.3.1 Verification by Classification. 1288.3.2 Verification by Regression 1289 AUTHORSHIP ATTRIBUTION USING CUSTOMIZED ASSOCIATIVE CLASSIFICATION 1319.1 PROBLEM STATEMENT 1329.1.1 Extracting Stylometric Features 1329.1.2 Associative Classification Writeprint 1339.1.3 Refined Problem Statement 1369.2 CLASSIFICATION BY MULTIPLE ASSOCIATION RULE FOR AUTHORSHIP ANALYSIS 1379.2.1 Mining Class Association Rules 1379.2.2 Pruning Class Association Rules 1399.2.3 Auth
Автор: Krishna P. Venkata, Gurumoorthy Sasikumar, Obaidat Mohammad S. Название: Social Network Forensics, Cyber Security, and Machine Learning ISBN: 9811314551 ISBN-13(EAN): 9789811314551 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses the issues and challenges in Online Social Networks (OSNs). It highlights various aspects of OSNs consisting of novel social network strategies and the development of services using different computing models. Moreover, the book investigates how OSNs are impacted by cutting-edge innovations.
Описание: As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions.
Описание: Deviance is both socially defined and influenced. While it is widely accepted that deviance is a social construction, this research revisits the conceptualization of deviance and advances the methods used to study deviance and social construction. This book presents and compares three methods for conceptualizing deviance within and across cultures. Comparing the United States to South Korea, perceptions of deviance are presented as how individuals define deviance and what acts are cited as deviant. Next, attribute data are used to assess differences in of conceptualizations of deviance by demographic factors. Finally, social network analysis is used to understand the social influences at work in how one perceives deviance. This book is unique in its reconceptualization of deviance and the application of social network analysis as a new tool for studying social influence and perceptions.
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