Noise Contrastive Estimation-based Matching Framework for Low-Resource Security Attack Pattern Recognition
This addresses the problem of low-resource security attack pattern recognition for cybersecurity analysts, offering a novel approach to TTP mapping.
The paper tackles the challenge of identifying Tactics, Techniques and Procedures (TTPs) in cybersecurity text by reformulating it as a semantic similarity matching problem instead of multi-class classification, resulting in improved learning despite large label spaces and skewed distributions.
Tactics, Techniques and Procedures (TTPs) represent sophisticated attack patterns in the cybersecurity domain, described encyclopedically in textual knowledge bases. Identifying TTPs in cybersecurity writing, often called TTP mapping, is an important and challenging task. Conventional learning approaches often target the problem in the classical multi-class or multilabel classification setting. This setting hinders the learning ability of the model due to a large number of classes (i.e., TTPs), the inevitable skewness of the label distribution and the complex hierarchical structure of the label space. We formulate the problem in a different learning paradigm, where the assignment of a text to a TTP label is decided by the direct semantic similarity between the two, thus reducing the complexity of competing solely over the large labeling space. To that end, we propose a neural matching architecture with an effective sampling-based learn-to-compare mechanism, facilitating the learning process of the matching model despite constrained resources.