Using Open Source Data to Identify Security Threats

Public and Private sector security analysts are charged with finding the needles in the haystack as they sift through an increasing tide of data to identify potential threats. Open source social media and online forums (such as Twitter and 4Chan) have become popular intelligence gathering sources, but as the data tide grows, it’s the algorithms that are being tasked with being the magnets that identify these “needles” and pick them out of the haystack.

To address this challenge, Opera Solutions has developed an algorithm they call SignalSensor, which uses machine intelligence to examine data streams to identify threats.  The tool chews through social network, online forums, and other open source commentary data in real-time, and using proprietary algorithms that help surface the threats, it presents them to analysts for action.

Opera Solutions says that the software is capable of processing over 200 million online elements in over 50 languages, and using ontology of 80 million terms and 420 million relationships, SignalSensor is able to identify and rank individual threats according to their severity.         

The technology, which relies on open source data streams to build the intelligence, applies outside of security and threat assessment, and is being used in marketing, finance, supply chain & operations and more. These advanced algorithms are presumably why Bangalore software exporter, Wipro, this week said it would be investing $30 million for a minority stake in Opera Solutions.

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