AI system predicts cyber attacks using input from human experts
Today’s security systems usually fall into one of two categories: man or machine. So-called “analyst-driven solutions” rely on rules created by human experts and therefore miss any attacks that don’t match the rules. Meanwhile, today’s machine-learning approaches rely on “anomaly detection,” which tends to trigger false positives that both create distrust of the system and end up having to be investigated by humans, anyway. But what if there was a solution that could merge those … More → Continue reading AI system predicts cyber attacks using input from human experts