Data science in cybersecurity and cyberthreat intelligence. ([2020])

• Record Type: Book
• Further Information: Note: Leslie F. Sikos, Kim-Kwang Raymond Choo, editors.
• Editors: Sikos, Leslie F; Choo, Kim-Kwang Raymond
• Contents: Intro -- Preface -- Contents -- About the Editors -- 1 The Formal Representation of Cyberthreats for Automated Reasoning -- 1.1 Introduction to Knowledge Organization in and Modeling of Cyberthreat Intelligence -- 1.2 Threat Classification -- 1.2.1 Attack Technique-Based Threat Classification -- 1.2.2 Threat Models for Threat Impact-Based Classification -- 1.2.3 Hybrid Models -- 1.3 Representing and Exchanging Cyberthreat Intelligence -- 1.3.1 Cyberthreat Taxonomies -- 1.3.2 Cyberthreat Ontologies -- 1.3.3 Utilizing the Formal Representation of Information Traversing Communication Networks in Cyberthreat Intelligence 1.4 Automated Reasoning over Formally Represented Threat Knowledge -- 1.5 Summary -- References -- 2 A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Preliminaries -- 2.3.1 Syntax -- 2.3.2 Semantics -- 2.4 Desired Technical Properties -- 2.5 A Novel Logic Programming-Based Cyberthreat Prediction System -- 2.5.1 Learner -- 2.5.2 Predictor -- 2.6 Data Description -- 2.6.1 Ground Truth -- 2.6.2 Hacker Community Discussions -- 2.7 Extracting Indicators of Cyberthreat -- 2.7.1 CVE to CPE Mappings -- 2.7.2 Extracting Entity Tags 2.8 Predicting Enterprise-Targeted Attacks -- 2.8.1 Setup -- 2.8.2 Evaluation -- 2.8.3 Results -- 2.9 Conclusion -- References -- 3 Discovering Malicious URLs Using Machine Learning Techniques -- 3.1 Introduction -- 3.2 Related Works -- 3.2.1 Malicious URL Detection -- 3.2.2 DGA Domain Detection -- 3.3 Tools and Data Sources -- 3.3.1 Web Client Honeypots -- 3.3.2 Web Crawlers -- 3.3.3 URL Datasets -- 3.3.4 Passive DNS Database -- 3.3.5 Search Engines -- 3.4 Machine Learning Techniques -- 3.4.1 Bayesian Sets -- 3.4.2 Other Machine Learning Algorithms -- 3.5 AutoBLG Framework -- 3.5.1 High-Level Overview -- 3.5.2 URL Expansion 3.5.3 URL Filtering -- 3.5.4 URL Verification -- 3.6 Evaluation -- 3.6.1 Preliminary Experiment -- 3.6.2 Performance of the AutoBLG Framework -- 3.6.3 Comparisons -- 3.7 Discussion -- 3.7.1 URL Expansion -- 3.7.2 Limitation of Query Patterns -- 3.7.3 URL Verification -- 3.7.4 Online Operation -- 3.8 Conclusion -- 3.9 Appendix -- References -- 4 Machine Learning and Big Data Processing for Cybersecurity Data Analysis -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Machine Learning Methods -- 4.4 Datasets, Architectures, and Experiments -- 4.4.1 Detection of Attacks Against IoT Structure 4.4.2 Detection of Host Scanning and DDoS Attacks -- 4.5 Conclusion -- References -- 5 Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Networks -- 5.1 Introduction -- 5.1.1 WSN -- 5.1.2 WBAN -- 5.1.3 PT -- 5.1.4 MC -- 5.1.5 CT -- 5.2 Threats and Attacks -- 5.2.1 Layer 1 (Physical Layer) -- 5.2.2 Layer 2 (Data Link Layer) -- 5.2.3 Layer 3 (Network Layer) -- 5.2.4 Layer 4 (Transport Layer) -- 5.2.5 Layers 5, 6, and 7 (Session, Presentation, and Application Layers) -- 5.3 Security Requirements for mHealth Devices -- 5.3.1 Confidentiality -- 5.3.2 Data Integrity -- 5.3.3 Availability -- 5.3.4 Privacy Policy
• Publisher Details: Cham : Springer
• Publication Date: 2020
• Extent: 1 online resource (140 pages)
• Subjects: 005.8; Computer security; Computer security; Electronic books
• Languages: English
• ISBNs: 9783030387884; 3030387887
• Related ISBNs: 9783030387877; 3030387879
• Notes: Note: Includes bibliographical references.; Note: Print version record.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.