Budapest Data 2015

Me and my team are currently developing a novel IT security product that employs user behavior analytics. With this product, security professionals can sustain a high level of security in complex IT environments by detecting abnormal activities that could indicate masquerade attacks, malicious insiders or other forms of security threats. As opposed to common SIEM (Security Information and Event Management) solutions that achieve this through comparing incoming activities to a manually defined rule database, our solution identifies reference patterns through unsupervised machine learning, providing more flexibility in specifying normal behavior. After this the ensemble of multiple algorithms scores incoming activities, highlighting those that differ most significantly from the previously learned baseline patterns.

I will present the most important high-level problems of this field, and I will also demonstrate the data science challenges that were translated from these issues. After defining these challenges, I will provide a broad perspective on the tools and algorithms that we develop and also methods that we utilize to resolve the challenges.