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A Faster Method for Bot Detection on Social Networks That Eliminates the Need for Expensive Algorithms

December 20, 2016 @ 12:00 - 13:00 EST

In this presentation, we present a novel approach to detection of bots on social networks in near real-time. Our approach comprises of computationally simple comparisons and calculations, as opposed to the all too common machine learning approach to this problem, or non-real-time approaches that involve network analysis which is both expensive and time-consuming.

The subset of bots this method focuses on, are those that can evade most, if not all, current detection methods. This is simply because they have little to no information associated with them that can be analyzed to make a determination on whether they are a bot or not. While it’s true that any account on a social network inherently has some information associated with it, it is very easy for this information to blend in with the masses of users who are simply “lurkers”. How can you determine if an account is a bot or not, especially when they don’t do anything? By the time they act, it’s too late to detect them. The only solution is a real-time, or near real-time, detection algorithm.

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Dr. Josh White
Dr. White is the Executive VP of Engineering for Rsignia Inc., engaged primarily in high-speed network, protocol, and data analytics for the defense community. He has worked as a Federal and State contractor under multiple agency contracts for the past 13 years, primarily in the areas of network defense, high-speed network analysis, large dataset processing, and high-performance computing.



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