Cyber forensics, 

Название: Digital forensics and cyber crime
ISBN: 3319255118 ISBN-13(EAN): 9783319255118
Издательство: Springer
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Описание: This book constitutes the refereed proceedings of the 7th International Conference on Digital Forensics and Cyber Crime, ICDF2C 2015, held in Seoul, South Korea, in October 2015.

Автор: Sanjay Goel
Название: Digital Forensics and Cyber Crime
ISBN: 3642115330 ISBN-13(EAN): 9783642115332
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Constitutes the post-conference proceedings of the First International ICST Conference, ICDF2C 2009, held September 30 - October 2, 2009, in Albany, NY, USA. These 16 papers present the whole gamut of multimedia and handheld device forensics, financial crimes, cyber crime investigations, forensics and law, cyber security, and information warfare.

Автор: Frank Breitinger; Ibrahim Baggili
Название: Digital Forensics and Cyber Crime
ISBN: 3030054861 ISBN-13(EAN): 9783030054861
Издательство: Springer
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Цена: 6988.00 р.
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Описание: This book constitutes the refereed proceedings of the 10th International Conference on Digital Forensics and Cyber Crime, ICDF2C 2018, held in New Orleans, LA, USA, in September 2018. The 11 reviewed full papers and 1 short paper were selected from 33 submissions and are grouped in topical sections on carving and data hiding, android, forensic readiness, hard drives and digital forensics, artefact correlation.

Автор: Khanna Kavita, Estrela Vania Vieira, Rodrigues Joel Josй Puga Coelho
Название: Cyber Security and Digital Forensics: Proceedings of Iccsdf 2021
ISBN: 9811639604 ISBN-13(EAN): 9789811639609
Издательство: Springer
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Цена: 27950.00 р.
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Описание: This book features high-quality research papers presented at the International Conference on Applications and Techniques in Cyber Security and Digital Forensics (ICCSDF 2021), held at The NorthCap University, Gurugram, Haryana, India, during April 3-4, 2021.

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
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Цена: 22359.00 р.
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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: Marcella Albert J.
Название: Cyber Forensics: Examining Emerging and Hybrid Technologies
ISBN: 036752418X ISBN-13(EAN): 9780367524180
Издательство: Taylor&Francis
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Цена: 16843.00 р.
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Описание: This book will also serve as a primary or supplemental text in both under- and post-graduate academic programs addressing information, operational and emerging technologies, cyber forensics, networks, cloud computing and cybersecurity.

Автор: Iqbal Farkhund, Debbabi Mourad, Fung Benjamin C. M.
Название: Machine Learning for Authorship Attribution and Cyber Forensics
ISBN: 3030616770 ISBN-13(EAN): 9783030616779
Издательство: Springer
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Цена: 22359.00 р.
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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: Matou?ek
Название: Digital Forensics and Cyber Crime
ISBN: 3319736965 ISBN-13(EAN): 9783319736969
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the refereed proceedings of the 9th International Conference on Digital Forensics and Cyber Crime, ICDF2C 2017, held in Prague, Czech Republic, in October 2017.

Автор: Chamundeswari Arumugam, Sanjay Misra, Saraswathi S, Suresh Jaganathan
Название: Confluence of AI, Machine, and Deep Learning in Cyber Forensics
ISBN: 1799858383 ISBN-13(EAN): 9781799858386
Издательство: Mare Nostrum (Eurospan)
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Цена: 22869.00 р.
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Описание: Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed.

Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.

Автор: Samer Al-khateeb; Nitin Agarwal
Название: Deviance in Social Media and Social Cyber Forensics
ISBN: 3030136892 ISBN-13(EAN): 9783030136895
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book describes the methodologies and tools used to conduct social cyber forensic analysis. By applying these methodologies and tools on various events observed in the case studies contained within, their effectiveness is highlighted. They blend computational social network analysis and cyber forensic concepts and tools in order to identify and study information competitors. Through cyber forensic analysis, metadata associated with propaganda-riddled websites are extracted. This metadata assists in extracting social network information such as friends and followers along with communication network information such as networks depicting flows of information among the actors such as tweets, replies, retweets, mentions, and hyperlinks. Through computational social network analysis, the authors identify influential actors and powerful groups coordinating the disinformation campaign. A blended social cyber forensic approach allows them to study cross-media affiliations of the information competitors. For instance, narratives are framed on blogs and YouTube videos, and then Twitter and Reddit, for instance, will be used to disseminate the message. Social cyber forensic methodologies enable researchers to study the role of modern information and communication technologies (ICTs) in the evolution of information campaign and coordination. In addition to the concepts and methodologies pertaining to social cyber forensics, this book also offers a collection of resources for readers including several datasets that were collected during case studies, up-to-date reference and literature surveys in the domain, and a suite of tools that students, researchers, and practitioners alike can utilize. Most importantly, the book demands a dialogue between information science researchers, public affairs officers, and policy makers to prepare our society to deal with the lawless “wild west” of modern social information systems triggering debates and studies on cyber diplomacy.

Автор: Chamundeswari Arumugam, Sanjay Misra, Saraswathi S, Suresh Jaganathan
Название: Confluence of AI, Machine, and Deep Learning in Cyber Forensics
ISBN: 1799849007 ISBN-13(EAN): 9781799849001
Издательство: Mare Nostrum (Eurospan)
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Цена: 30215.00 р.
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Описание: Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed.

Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.

Автор: Greg Gogolin
Название: Digital forensics explained
ISBN: 0367503433 ISBN-13(EAN): 9780367503437
Издательство: Taylor&Francis
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Цена: 8573.00 р.
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Описание: Digital Forensics Explained, 2nd edition, covers the full life cycle of conducting a mobile and computer digital forensic examinations including planning and performing an investigation as well as report writing and testifying.