"Today's security systems usually fall into one of two categories: man or machine," Adam Conner-Simon from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) wrote in a post on the MIT News site. "So-called'analyst-driven solutions' rely on rules created by human experts and therefore miss any attacks that don't match the rules," he said. "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." The MIT and PatternEx platform attempts to merge those two approaches. AI2 predicts attacks by combing through data and detecting suspicious activity by clustering it into meaningful patterns using unsupervised machine learning, according to researchers at MIT.