Cyber security is an ongoing problem, especially as our technology becomes a larger part of our everyday lives. Neither our efforts alone nor artificial intelligence (AI) have achieved a fool proof method. Our human-driven techniques are based on rules set by living experts, which means we miss any threats that don’t follow these rules. Machine-learning approaches, on the other hand, rely on anomaly detection, which often trigger “false positives” (nonthreats mistakenly identified as threats) that need to be checked by humans.
What if humans joined forces with AI? Researchers at MIT have been working on this concept and have produced some promising results.
AI² is a collaboration between researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the startup company PatternEx. After three months of testing on 3.6 billion pieces of user-made data, it is said to detect 85% of attacks and reduce the number of false positives by a factor of five.
To find the most important potential problems, AI² uses machine learning first, and then shows the top events to analysts for labeling. After the analysts go through and confirm which events are actual attacks, the system incorporates this feedback into its models for the next set of data. This ongoing cycle of improvement decreases potential events and increases accuracy.
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