Algorithm learns to identify anomalous activity online with high degree of accuracy
At the IEEE International Conference on Big Data Security in New York City this month, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine learning start-up PatternEx, presented a paper about their new security system that combines machine learning approaches and input from human security experts. This system, called AI2 (named by merging “artificial intelligence” and “analyst intuition”), has an 85 percent success rate in identifying threats and a false positive rate of 4.4 percent over a raw data set of 3.6 billion log lines. According to the paper, the three major challenges faced by the security industry are a lack of labelled examples to model learning models on, constant evolution of attacker’s methods, and limited reliance on security analysts to determine each threat’s risk factor.In…
Link to Full Article: Algorithm learns to identify anomalous activity online with high degree of accuracy
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