MIT Researchers Develop AI Cybersecurity Platform -- Campus Technology

Researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) have developed a cybersecurity system that combines human and machine-learning approaches to reduce cyber attacks and false positives. Named AI2 to signify that it merges artificial intelligence with "analyst intuition," the system was developed by Kalyan Veeramachaneni, a research scientist at CSAIL, and Ignacio Arnaldo, a former postdoctoral researcher at CSAIL who is now a chief data scientist at PatternEx. In tests, the researchers demonstrated that "AI2 can detect 85 percent of attacks, which is roughly three times better than previous benchmarks, while also reducing the number of false positives by a factor of five," according to a news release from CSAIL. Most modern cybersecurity systems use either analyst-driven solutions or machine-learning approaches. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.