Argyle Data, a leader in big data/machine learning analytics for mobile providers, has highlighted the role of supervised and unsupervised machine learning in detecting and preventing anomalous mobile traffic. The move comes as Argyle Data and Carnegie Mellon University (CMU) Silicon Valley's Department of Electrical and Computer Engineering prepare to publish a new research paper on anomaly detection, which will be presented at academic conferences during the first half of 2017. Global mobile fraud levels cost the industry an estimated U.S. 38 billion 2015 according to the latest CFCA survey. Most major attacks today are'fraud cocktails': unpredictable mixtures of several fraud types. The chief reason that operators are unable to detect complex new fraud is that approaches currently used to detect fraud in communications networks typically rely on static rules with pre-set thresholds, and can only detect known fraud types.
Aug-14-2016, 17:30:37 GMT