Dr. Michael Fire (Courtesy Ben-Gurion University)
Dr. Michael Fire of Ben-Gurion University in Be’er Sheva has come up with a new breakthrough way to track anonymous and fictitious users. These users, known as “anomalous” (meaning outside the norm or expected) pose a serious threat to the cyber security of any organization and are getting to be all too common.
Ben-Gurion University explained that malicious or fictitious users on internet networks have become the “bane of the internet’s existence” and that many “bemoan their increasing frequency.”
The problem is so great that now there are all sorts of tools for sale online that can be used to detect these groups of anomalous users on internet networks. Malicious users are the ones who try to embed all manner of spyware and ransomware, whether on a big system used by an organization or on someone’s personal computer. Sometimes this is done just for fun, but hackers also do this to make money by demanding a ransom in exchange for returning control of the system to its owner or to not release publicly private information.
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Fictitious users on social networks try to spread fake news and false information, like what happened during the 2020 U.S. Presidential election and during the Covid crisis. And they also try to find personal information on people’s social media accounts in order to steal passwords and so forth.
Well, now Dr. Michael Fire, says the University, has developed a new method to detect groups of anomalous users. His findings were recently published in the peer-reviewed journal Neural Processing Letters.
“The advantage of this study is that we can detect anomalous groups of users (such as groups of fake profiles) rather than single users. Uncovering groups of fake profiles is a challenging and less explored task,” says Dr. Michael Fire, head of the Data4Good Lab and a member of the Department of Software and Information Systems Engineering.
“Our method is generic. Therefore, it can potentially work on different types of social media platforms. We tested it on several different types of networks, such as Reddit and Wikipedia (which is also a type of social network),” explains Dr. Fire.
After testing their method on randomly generated networks and real-world networks, they found that it outperformed many other methods in a range of settings.
Their method is better than other methods already out there because “Our method is based solely on network structural properties. That makes our method independent of vertices’ attributes (the connections between users online). Thus, it is agnostic to the domain. When comparing our algorithm with other algorithms, it performed better on simulation and real-world data in many cases. It successfully detected groups of anomalous users’ communities who presented peculiar online activity,” says Dr. Fire.
