MIT boffins build AI bot that spots ’85 per cent’ of hacker invasions

Eggheads at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have trained a machine-learning system to detect 85 per cent of network attacks. To reach that level, the software, dubbed AI2 [PDF], parsed billions of lines of log files, looking for behaviors that indicate either a malware infection or a human hacker trying to get into a network. If it spotted any suspicious connections or packets, it alerted a human analyst, who identified whether the software got it right or wrong. After 3.6 billion log lines were scanned and three months of training passed, the AI2 system was able to hit 85 per cent accuracy in detecting malicious activity, we’re told. “This brings together the strengths of analyst intuition and machine learning,” said Nitesh Chawla, the Frank M. Freimann Professor…


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