The Path To Autonomous Cyber Defense

1 code implementation • 12 Apr 2024 • Sean Oesch, Phillipe Austria, Amul Chaulagain, Brian Weber, Cory Watson, Matthew Dixson, Amir Sadovnik

Defenders are overwhelmed by the number and scale of attacks against their networks. This problem will only be exacerbated as attackers leverage artificial intelligence to automate their workflows.

Toward the Detection of Polyglot Files

no code implementations • 14 Mar 2022 • Luke Koch, Sean Oesch, Mary Adkisson, Sam Erwin, Brian Weber, Amul Chaulagain

To address the problem of polyglot detection we assembled a data set using the mitra tool.

Malware Detection

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection

1 code implementation • 16 Dec 2020 • Robert A. Bridges, Sean Oesch, Miki E. Verma, Michael D. Iannacone, Kelly M. T. Huffer, Brian Jewell, Jeff A. Nichols, Brian Weber, Justin M. Beaver, Jared M. Smith, Daniel Scofield, Craig Miles, Thomas Plummer, Mark Daniell, Anne M. Tall

In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files?

Malware Detection

An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center

no code implementations • 16 Dec 2020 • Sean Oesch, Robert Bridges, Jared Smith, Justin Beaver, John Goodall, Kelly Huffer, Craig Miles, Dan Scofield

Gartner, a large research and advisory company, anticipates that by 2024 80% of security operation centers (SOCs) will use machine learning (ML) based solutions to enhance their operations.

BIG-bench Machine Learning