Visible to the public Practical Attacks Against Graph-based Clustering

TitlePractical Attacks Against Graph-based Clustering
Publication TypeConference Paper
Year of Publication2017
AuthorsChen, Yizheng, Nadji, Yacin, Kountouras, Athanasios, Monrose, Fabian, Perdisci, Roberto, Antonakakis, Manos, Vasiloglou, Nikolaos
Conference NameProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-4946-8
KeywordsAdversarial Machine Learning, controller area network security, DGA, Network security, pubcrawl, resilience, Resiliency, unsupervised learning
AbstractGraph 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.
Citation Keychen_practical_2017