| Title | Practical Attacks Against Graph-based Clustering |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Chen, Yizheng, Nadji, Yacin, Kountouras, Athanasios, Monrose, Fabian, Perdisci, Roberto, Antonakakis, Manos, Vasiloglou, Nikolaos |
| Conference Name | Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4946-8 |
| Keywords | Adversarial Machine Learning, controller area network security, DGA, Network security, pubcrawl, resilience, Resiliency, unsupervised learning |
| Abstract | Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible. |
| URL | http://doi.acm.org/10.1145/3133956.3134083 |
| DOI | 10.1145/3133956.3134083 |
| Citation Key | chen_practical_2017 |
Practical Attacks Against Graph-based Clustering