How CrowdStrike Uses Similarity-Based Mapping to Understand Cybersecurity Data and Prevent Breaches

CrowdStrike has introduced a new similarity-based mapping paradigm to enhance cybersecurity data management and breach prevention. This approach organizes complex cybersecurity data, including files, network traffic, and behavioral sequences, by associating disparate representations of these objects to make them accessible, searchable, and mappable. By leveraging this new model, analysts can prevent breaches more effectively by developing maps that coalesce different object representations and facilitating cross-paradigm inference. The initiative aims to improve the classification of benign and malicious objects using machine learning classifiers and optimized parsers, ultimately providing a more comprehensive understanding of cybersecurity threats. This effort contributes to CrowdStrike's broader commitment to advancing cybersecurity through innovation and public sharing of research findings.