Computer scientists from the Michigan Institute of Technology (MIT) and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts.
At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules.
But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway.
To improve this, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.
Rather than requiring cybersecurity analysts to spend all day analysing huge amounts of data that may or may not be a sign that cybercriminals are attacking a network, AI2 is instead trained to pick out the 200 most abnormal events it has detected during that day.