Adversarial Machine Learning for Fraud Detection: How Can Organizations Benefit from the Pioneering Work of the NSA and Facebook?
How is technology evolving to analyze multiple and massive streams of data in real time to detect fraudulent activity? The NSA has pioneered data collection techniques at a staggering scale, potentially monitoring all activity for an entire country. Facebook has pioneered adversarial machine learning fraud detection into an "immune system" that can carry out tens of billions of checks per day to find patterns where the fraudsters are purposefully trying not to create any. We will discuss how the combination and augmentation of these technologies with deep packet inspection enables organizations to deploy them. We will also learn how such a solution could:
See Also: Redefining Security Analytics
- Analyze sparsely populated data sets that may have millions of distinct features;
- Learn what a negative pattern is where none existed before;
- Perform this analysis in real time and at a massive scale.
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