| Title | Combinatorial Code Classification Amp; Vulnerability Rating |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Barr, Joseph R., Shaw, Peter, Abu-Khzam, Faisal N., Yu, Sheng, Yin, Heng, Thatcher, Tyler |
| Conference Name | 2020 Second International Conference on Transdisciplinary AI (TransAI) |
| Keywords | Bluetooth, clique decomposition, cluster-editing, code2vec, Computational modeling, Computer crime, Computers, cybersecurity, Deep Learning, graph theory, Human Behavior, LSTM, Malware, malware analysis, Metrics, parametrized complexity, privacy, pubcrawl, random forests, resilience, Resiliency, static code analysis, SVE |
| Abstract | Empirical analysis of source code of Android Fluoride Bluetooth stack demonstrates a novel approach of classification of source code and rating for vulnerability. A workflow that combines deep learning and combinatorial techniques with a straightforward random forest regression is presented. Two kinds of embedding are used: code2vec and LSTM, resulting in a distance matrix that is interpreted as a (combinatorial) graph whose vertices represent code components, functions and methods. Cluster Editing is then applied to partition the vertex set of the graph into subsets representing nearly complete subgraphs. Finally, the vectors representing the components are used as features to model the components for vulnerability risk. |
| DOI | 10.1109/TransAI49837.2020.00017 |
| Citation Key | barr_combinatorial_2020 |
Combinatorial Code Classification Amp; Vulnerability Rating