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Security: Security Experts Paired with Self-Learning AI Algorithms Battle Cyber Crimes
MIT AI Laboratory (CSAIL) scientists are using machine learning to help stop cybercrimes. The MIT algorithm called AI2 (AI squared) reportedly is three times more effective in spotting cyber activities than other methods. The algorithm boasts of being able to detect 85 percent of cyber crimes.
Kalyan Veeramachaneni, a research scientist at MIT’s CSAIL, said that “You can think about the system as a virtual analyst. It continuously generates new models that it can refine in as little as a few hours, meaning it can improve its detection rates significantly and rapidly. The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions. That human-machine interaction creates a beautiful, cascading effect.”
The algorithm works in tandem with a security expert. The algorithm identifies possible abnormal events that have been logged in the system and passes them to the expert who in turn scores and identifies those that are actual attacks versus the false positives. Over time, the algorithm learns and becomes increasingly more accurate in its ability to spot and alert for potential problems.
Ignacio Arnaldo, former CSAIL postdoc , said that “unless you have that access, you can’t truly develop these methods. Startups like PatternEx are the perfect environment to develop this kind of technology.”
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[…] One organization that is an example of how fast new developments can be rolled out is MIT’s CSAIL (Computer Science and Artificial Intelligence Laboratory). We’ve written in the past about work at CSAIL in the areas of Artificial Intelligence and Advanced Security. […]