This book presents the development of a classification scheme to organize and represent ransomware threat knowledge through the implementation of an innovative methodology centered around the semantic annotation of domain-specific source documentation. By combining principles from Computer Science, document management, and semantic data processing, the research establishes an innovative framework to organize ransomware data extracted from specialized source texts in a systematic classification system. Through detailed chapters, the book explores the process of applying semantic annotation to a specialized corpus comprising CVE prose descriptions linked to known ransomware threats. This approach not only organizes but also deeply analyzes these descriptions, uncovering patterns and vulnerabilities within ransomware operations. The book presents a pioneering methodology that integrates CVE descriptions with ATT and CK frameworks, significantly refining the granularity of threat intelligence. The insights gained from a pattern-based analysis of vulnerability-related documentation are structured into a hierarchical model within an ontology framework, enhancing the capability for predictive operations. This model prepares cybersecurity professionals to anticipate and mitigate risks associated with new vulnerabilities as they are cataloged in the CVE list, by identifying recurrent characteristics tied to specific ransomware and related vulnerabilities. With real-world examples, this book empowers its readers to implement these methodologies in their environments, leading to improved prediction and prevention strategies in the face of growing ransomware challenges.

Ransomware Analysis: Knowledge Extraction PDF

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