Machine Learning to Reduce False Positives
“…The goal, according to Novikov, is to train a neural network to improve existing algorithms. Traditional false positive response processes–like CAPTCHA or email alerts to IT support teams—could be replaced with automatic rule tuning by the machine learning network. The trick is to get the machine learning system to be indifferent to the detection logic that made the initial decision. Novikov stressed that the goal is not to create yet another form of detection logic. The new goal is learning, adapting, and responding better with each iterated threat or false positive…” is covered in this article, Using Machine Learning to Reduce False Positives, by Tony Bradley.