Abstract
Abstract This chapter examines how natural language processing can be applied for building rich models for cybersecurity analytics. For this, it applies text mining to the natural-language content of Common Attack Pattern Enumeration and Classification (CAPECTM), a standardized corpus of cyberattack patterns. We adopt a vector-space model in which CAPEC attack patterns are treated as documents with term vectors. This provides a space in which to define distance measures, such as for retrieving attack patterns through term queries or finding clusters of related attack patterns. Analysis of clustering patterns, i.e., cluster hierarchies (clusters within clusters) is aided through tree visualization techniques. These analytic and visual techniques provide a range of capabilities for leveraging the content and relationships in CAPEC, e.g., for building more complex security models such as network attack graphs.
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