Bryn Marie Reinstadler

Erik Hemberg, Jonathan Kelly, Michal Shlapentokh-Rothman et al.

Many public sources of cyber threat and vulnerability information exist to help defend cyber systems. This paper links MITRE's ATT&CK MATRIX of Tactics and Techniques, NIST's Common Weakness Enumerations (CWE), Common Vulnerabilities and Exposures (CVE), and Common Attack Pattern Enumeration and Classification list (CAPEC), to gain further insight from alerts, threats and vulnerabilities. We preserve all entries and relations of the sources, while enabling bi-directional, relational path tracing within an aggregate data graph called BRON. In one example, we use BRON to enhance the information derived from a list of the top 10 most frequently exploited CVEs. We identify attack patterns, tactics, and techniques that exploit these CVEs and also uncover a disparity in how much linked information exists for each of these CVEs. This prompts us to further inventory BRON's collection of sources to provide a view of the extent and range of the coverage and blind spots of public data sources.

Jacob M. Springer, Bryn Marie Reinstadler, Una-May O'Reilly

Neural networks are well-known to be vulnerable to imperceptible perturbations in the input, called adversarial examples, that result in misclassification. Generating adversarial examples for source code poses an additional challenge compared to the domains of images and natural language, because source code perturbations must retain the functional meaning of the code. We identify a striking relationship between token frequency statistics and learned token embeddings: the L2 norm of learned token embeddings increases with the frequency of the token except for the highest-frequnecy tokens. We leverage this relationship to construct a simple and efficient gradient-free method for generating state-of-the-art adversarial examples on models of code. Our method empirically outperforms competing gradient-based methods with less information and less computational effort.