AI System Predicts 85 Percent of Cyberattacks 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 two worlds? What would it look like? In a new paper, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine­ learning start­up PatternEx demonstrate an artificial­ intelligence platform called “AI2” that predicts cyber­ attacks significantly better than existing systems by continuously incorporating input from human experts. (The name…


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