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| PatternEx’s Virtual Analyst Platform uses machine learning models to detect suspicious activity on a network. That activity is then presented to human analysts for feedback that improves the systems’ ability to flag activity analysts care about. |
Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that “needle” and stop it before it does any damage.
The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.
MIT startup PatternEx starts with the assumption that algorithms can’t protect a system on their own. The company has developed a closed loop approach whereby machine-learning models flag possible attacks and human experts provide feedback...
Giving security analysts an army
PatternEx’s Virtual Analyst Platform is designed to make security analysts feel like they have an army of assistants combing through data logs and presenting them with the most suspicious behavior on their network.
The platform uses machine learning models to go through more than 50 streams of data and identify suspicious behavior. It then presents that information to the analyst for feedback, along with charts and other data visualizations that help the analyst decide how to proceed. After the analyst determines whether or not the behavior is an attack, that feedback is incorporated back into the models, which are updated across PatternEx’s entire customer base.
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Source: MIT News
