System predicts 85 percent of cyber attacks using input from human experts

It then presents this activity to human analysts, who confirm which events are actual attacks, and incorporate that feedback into its models for the next set of data. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup PatternEx have developed an AI platform called AI2 that predicts cyber-attacks significantly better than existing systems by continuously incorporating input from human experts (AI2 refers to merging AI with "analyst intuition": rules created by living experts). The team showed that AI2 can detect 85 percent of attacks --about three times better than previous benchmarks -- while also reducing the number of false positives by a factor of 5. The system was tested on 3.6 billion pieces of data known as "log lines," which were generated by millions of users over a period of three months. To predict attacks, AI2 combs through data and detects suspicious activity by clustering the data into meaningful patterns using unsupervised (automatic, no human help) machine learning.