Credit card frauds are unauthorized transactions that are made or attempted by a person or an organization that is not authorized by the card holders. In addition to machine learning-based techniques, credit card companies often employ domain experts to manually specify rules that exploit domain knowledge for improving the detection process. Over time, however, as new (fraudulent and legitimate) transaction arrive, these rules need to be updated and refined to capture the evolving (fraud and legitimate) activity patterns. The goal of the RUDOLF system that is demonstrated here is to guide and assist domain experts in this challenging task. RUDOLF automatically determines a best set of candidate adaptations to existing rules to capture all fraudulent transactions and, respectively, omit all legitimate transactions. The proposed modifications can then be further refined by domain experts based on their domain knowledge, and the process can be repeated until the experts are satisfied with the resulting rules. Our experimental results on real-life datasets demonstrate the effectiveness and efficiency of our approach. We showcase RUDOLF with two demonstration scenarios: detecting credit card frauds and network attacks. Our demonstration will engage the VLDB audience by allowing them to play the role of a security expert, a credit card fraudster, or a network attacker.
|Number of pages||4|
|Journal||Proceedings of the VLDB Endowment|
|State||Published - 2015|
|Event||42nd International Conference on Very Large Data Bases, VLDB 2016 - New Delhi, India|
Duration: 5 Sep 2016 → 9 Sep 2016