At Haystax, we’re as passionate about data science as we are about software development. Data science underpins the security analytics software we build, most notably in providing the theoretical and mathematical foundations for our probabilistic model-based approach — and in the machine-learning algorithms and other artificial intelligence techniques that we fuse with our models — to help organizations to pinpoint and respond to their most serious threats.
This collection of research papers is intended to reveal some of the data science that has gone into what we call our Carbon model of whole-person behavior, a Bayesian inference network that is at the heart of the Haystax security analytics platform we have deployed in support of operational missions such as insider threat analysis, continuous monitoring of cleared personnel and cyber fraud detection. The papers were written and published over a period of three years, starting in 2014. Four of the five were peer reviewed and presented at leading conferences around the U.S., and one garnered an award when it was presented.
We hope you find the material in this collection informative, and useful — even inspiring — in your own work. The data scientists and software engineers who contributed to these papers have certainly inspired us, as we have transformed their knowledge and expertise into ever evolving operational solutions that address complex real-world problems for the individuals responsible for protecting the security of our nation and the safety of its people.
Read the following peer-reviewed papers from Haystax data scientists: