This should be very cool, offering more details of the kind that Elie Bersztein talked about at Usenix Enigma (Gmail’s spam and virus filters also use tensorflow.)
There seems to be very little overlap currently between the worlds of infosec and machine learning. If a data scientist attended Black Hat and a network security expert went to NIPS, they would be equally at a loss. This is unfortunate because infosec can definitely benefit from a probabilistic approach but a significant amount of domain expertise is required in order to apply ML methods.Machine learning practitioners face a few challenges for doing work in this domain including understanding the datasets, how to do feature engineering (in a generalizable way) and creation of labels.
To address some of the issues unique to adversarial machine learning, Startup.ML is organizing a one-day special conference on September 10th in San Francisco. Leading practitioners from Google, Coinbase, Ripple, Stripe, Square, etc. will cover their approaches to solving these problems in hands-on workshops and talks. The conference will also include a hands-on, 90 minute tutorial on TensorFlow by Illia Polosukhin one of the most active contributors to Google’s new deep learning library. Reference Franc, Vojtech, Michal Sofka, and Karel Bartos. “Learning detector of malicious network traffic from weak labels.” Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015.Seni, Giovanni