BreachRadar: Automatic Detection of Points-of-Compromise [chapter]

Miguel Araujo, Miguel Almeida, Jaime Ferreira, Luis Silva, Pedro Bizarro
2017 Proceedings of the 2017 SIAM International Conference on Data Mining  
Bank transaction fraud results in over 13B annual losses for banks, merchants, and card holders worldwide. Much of this fraud starts with a Point-of-Compromise (a data breach or a skimming operation) where credit and debit card digital information is stolen, resold, and later used to perform fraud. We introduce this problem and present an automatic Points-of-Compromise (POC) detection procedure. BreachRadar is a distributed alternating algorithm that assigns a probability of being compromised
more » ... the different possible locations. We implement this method using Apache Spark and show its linear scalability in the number of machines and transactions. BreachRadar is applied to two datasets with billions of real transaction records and fraud labels where we provide multiple examples of real Points-of-Compromise we are able to detect. We further show the effectiveness of our method when injecting Points-of-Compromise in one of these datasets, simultaneously achieving over 90
doi:10.1137/1.9781611974973.63 dblp:conf/sdm/AraujoAFSB17 fatcat:ygq5scwkvbda3aqjntz6qrldq4