System predicts 85 percent of cyber-attacks using input from human experts
Today’s security systems usually fall into one of two categories: human or machine. So-called “analyst-driven solutions” rely on rules created by living 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 were 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 comes from merging artificial intelligence…
Link to Full Article: System predicts 85 percent of cyber-attacks using input from human experts
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