Dean Teffer is Principal Scientist for Machine Learning at JASK, which delivers the first AI-powered SOC platform that enables autonomous security operations. At JASK he is working with a highly talented team of developers, analysts and scientists to create a new type of network security product. Dean designs and develops model-driven machine learning systems for streaming and distributed production environments, with his specialties including anomaly detection, probabilistic modeling and clustering. Dean has worked for the past 8 years in network security, and his teams have made significant contributions in early detection for counter intelligence applications, among other achievements.
Dean previously served in program management roles at the UT Austin Applied Research Labs, Siemens, Precision Traffic Systems and Compuware. He has a PhD in Computer Engineering and an Masters in Physics from the University of Texas at Austin.
Compared to even just a few years ago, the tools available for data scientists and machine learning engineers today are of remarkable variety and ease of use.
Machine learning is to pick a model and perform parameter estimation to yield a fitted model.
In order to provide clarity and perspective, this blog will define AI and machine learning and their respective relationship.
If Machine Learning methods can drive a car, surely they can help in the SOC and automate processes.
Hash matching or regex rules are the main type of signature detection, but some behavioral detection are also.
Artificial Intelligence is not intended to fully-automate threat mitigation and response.