Machine Learning For Authorship Attribution And Cyber Forensics - Iqbal Farkhund; Debbabi Mourad; Fung Benjamin C. M. | Libro Springer 12/2020 - HOEPLI.it


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iqbal farkhund; debbabi mourad; fung benjamin c. m. - machine learning for authorship attribution and cyber forensics

Machine Learning for Authorship Attribution and Cyber Forensics

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 12/2020
Edizione: 1st ed. 2020





Trama

The book first explores the cybersecurity’s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes.

Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals.

Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law. 






Sommario


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 29
1.5.3 Online Anonymity
1.5.4 Organized Crime 30
1.5.5 Nation State Threat Actors 31
1.6 CYBERCRIME INVESTIGATION 32
2 MACHINE LEARNING FRAMEWORK FOR MESSAGING FORENSICS 34
2.1 SOURCES OF CYBERCRIMES 36
2.2 FEW ANALYSIS TOOLS AND TECHNIQUES 38
2.3 PROPOSED FRAMEWORK FOR CYBERCRIMES INVESTIGATION 39
2.4 AUTHORSHIP ANALYSIS 41
2.5 INTRODUCTION TO CRIMINAL INFORMATION MINING 43
2.5.1 Existing Criminal Information Mining Approaches 44
2.5.2 WordNet-based Criminal Information Mining 47
2.6 WEKA 48
3 HEADER-LEVEL INVESTIGATION AND ANALYZING NETWORK INFORMATION 50
3.1 STATISTICAL EVALUATION 52
3.2 TEMPORAL ANALYSIS 53
3.3 GEOGRAPHICAL LOCALIZATION 53
3.4 SOCIAL NETWORK ANALYSIS 55
3.5 CLASSIFICATION 56
3.6 CLUSTERING 58
4 AUTHORSHIP ANALYSIS APPROACHES 59
4.1 HISTORICAL PERSPECTIVE 59
4.2 ONLINE ANONYMITY AND AUTHORSHIP ANALYSIS 60
4.3 STYLOMETRIC FEATURES 61
4.4 AUTHORSHIP ANALYSIS METHODS 63
4.4.1 Statistical Analysis Methods 64
4.4.2 Machine Learning Methods 64
4.4.1 Classification Method Fundamentals 66
4.5 AUTHORSHIP ATTRIBUTION 67
4.6 AUTHORSHIP CHARACTERIZATION 69
4.7 AUTHORSHIP VERIFICATION 70
4.8 LIMITATIONS OF EXISTING AUTHORSHIP TECHNIQUES 72
5 AUTHORSHIP ANALYSIS - WRITEPRINT MINING FOR AUTHORSHIP ATTRIBUTION 74
5.1 AUTHORSHIP ATTRIBUTION PROBLEM 78
5.1.1 Attribution without Stylistic Variation 79
5.1.2 Attribution with Stylistic Variation 79
5.2 BUILDING BLOCKS OF THE PROPOSED APPROACH 80
5.3 WRITEPRINT 87
5.4 PROPOSED APPROACHES 87
5.4.1 AuthorMiner1: Attribution without Stylistic Variation 88
5.4.2 AuthorMiner2: Attribution with Stylistic Variation 92
6 AUTHORSHIP ATTRIBUTION WITH FEW TRAINING SAMPLES 97
6.1 PROBLEM STATEMENT AND FUNDAMENTALS 100
6.2 PROPOSED APPROACH 101
6.2.1 Preprocessing 101
6.2.2 Clustering by Stylometric Features 102
6.2.3 Frequent Stylometric Pattern Mining 104
6.2.4 Writeprint Mining 105
6.2.5 Identifying Author 106
6.3 EXPERIMENTS AND DISCUSSION 106
7 AUTHORSHIP CHARACTERIZATION 113
7.1 PROPOSED APPROACH 115
7.1.1 Clustering Anonymous Messages 116
7.1.2 Extracting Writeprints from Sample Messages 116
7.1.3 Identifying Author Characteristics 116
7.2 EXPERIMENTS AND DISCUSSION 117
8 AUTHORSHIP VERIFICATION 120
8.1 PROBLEM STATEMENT 123
8.2 PROPOSED APPROACH 125
8.2.1 Verification by Classification 126
8.2.2 Verification by Regression 126
8.3 EXPERIMENTS AND DISCUSSION 127
8.3.1 Verification by Classification. 128
8.3.2 Verification by Regression 128
9 AUTHORSHIP ATTRIBUTION USING CUSTOMIZED ASSOCIATIVE CLASSIFICATION 131
9.1 PROBLEM STATEMENT 132
9.1.1 Extracting Stylometric Features 132
9.1.2 Associative Classification Writeprint 133
9.1.3 Refined Problem Statement 136
9.2 CLASSIFICATION BY MULTIPLE ASSOCIATION RULE FOR AUTHORSHIP ANALYSIS 137
9.2.1 Mining Class Association Rules 137
9.2.2 Pruning Class Association Rules 139
9.2.3 Authorship Classification 142
9.3 EXPERIMENTAL EVALUATION 145
10 CRIMINAL INFORMATION MINING 151
10.1 PROBLEM STATEMENT 153
10.1.1 Subproblem: Clique Mining 154
10.1.2 Subproblem: Concept Analysis 156
10.2 PROPOSED APPROACH 156
10.2.1 Clique Miner 157
10.2.2 Concept Miner 160
10.2.3 Information Visualizer 165
10.3 EXPERIMENTS AND DISCUSSION 166
11 ARTIFICIAL INTELLIGENCE AND DIGITAL FORENSICS 172
11.1 AI TECHNIQUES 173
11.2 DEEP LEARNING FOR SOCIAL MEDIA MINING 176
11.2.1 Tweet Crawler 178
11.2.2 Preprocessing 178
11.2.3 Event Identifier 178
11.2.4 Event Filter 180
11.2.5 Information Extractor 180
11.3 FUTURE APPLICATION AND IMPLICATIONS FOR DIGITAL FORENSICS 183








Altre Informazioni

ISBN:

9783030616748

Condizione: Nuovo
Collana: International Series on Computer Entertainment and Media Technology
Dimensioni: 235 x 155 mm Ø 424 gr
Formato: Copertina rigida
Illustration Notes:10 Illustrations, black and white
Pagine Arabe: 158
Pagine Romane: ix






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