Spatial Data Analysis as a Software Quality Modeling Technique. - Justin M. Beaver; Guy A. Schiavone (SEKE, 2003)

A Comparison of Software Quality Modeling Techniques. - Justin M. Beaver; Guy A. Schiavone (Software Engineering Research and Practice, 2003)

Predicting software suitability using a Bayesian belief network. - Justin M. Beaver; Guy A. Schiavone; Joseph Berrios (ICMLA, 2005)

The effects of development team skill on software product quality. - Justin M. Beaver; Guy A. Schiavone (ACM SIGSOFT Softw. Eng. Notes, 2006)

Dimensionality reduction particle swarm algorithm for high dimensional clustering. - Xiaohui Cui; Justin M. Beaver; Jesse St. Charles; Thomas E. Potok (SIS, 2008)

A Stigmergy Approach for Open Source Software Developer Community Simulation. - Xiaohui Cui; Justin M. Beaver; Jim N. Treadwell; Thomas E. Potok; Laura Pullum (CSE (4), 2009)

An information fusion framework for threat assessment. - Justin M. Beaver; Ryan A. Kerekes; Jim N. Treadwell (FUSION, 2009)

Modeling success in FLOSS project groups. - Justin M. Beaver; Xiaohui Cui; Jesse St. Charles; Thomas E. Potok (PROMISE, 2009)

The Swarm Model in Open Source Software Developer Communities. - Xiaohui Cui; Justin M. Beaver; Everett Stiles; Laura Pullum; Brian A. Klump; Jim N. Treadwell; Thomas E. Potok (SocialCom/PASSAT, 2010)

Construction of Synthetic Populations with Key Attributes: Simulation Set-Up While Accommodating Multiple Approaches within a Flexible Simulation Platform. - Steven J. Fernandez; Amy N. Rose; Edward A. Bright; Justin M. Beaver; Christopher T. Symons; Olufemi A. Omitaomu; Cathy Jiao (SocialCom/PASSAT, 2010)

An approach to the automated determination of host information value. - Justin M. Beaver; Robert M. Patton; Thomas E. Potok (CICS, 2011)

Visual Mining Intrusion Behaviors by Using Swarm Technology. - Xiaohui Cui; Justin M. Beaver; Thomas E. Potok; Li Yang 001 (HICSS, 2011)

Classification of Distributed Data Using Topic Modeling and Maximum Variation Sampling. - Robert M. Patton; Justin M. Beaver; Thomas E. Potok (HICSS, 2011)

Hierarchical clustering and visualization of aggregate cyber data. - Robert M. Patton; Justin M. Beaver; Chad A. Steed; Thomas E. Potok; Jim N. Treadwell (IWCMC, 2011)

Nonparametric semi-supervised learning for network intrusion detection: combining performance improvements with realistic in-situ training. - Christopher T. Symons; Justin M. Beaver (AISec, 2012)

ShadowNet: An Active Defense Infrastructure for Insider Cyber Attack Prevention. - Xiaohui Cui; Wade Gasior; Justin M. Beaver; Jim N. Treadwell (ICCSA (4), 2012)

A learning system for discriminating variants of malicious network traffic. - Justin M. Beaver; Christopher T. Symons; Robert E. Gillen (CSIIRW, 2013)

An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications. - Justin M. Beaver; Raymond C. Borges-Hink; Mark A. Buckner (ICMLA (2), 2013)

Matisse: A visual analytics system for exploring emotion trends in social media text streams. - Chad A. Steed; Margaret Drouhard; Justin M. Beaver; Joshua Pyle; Paul Logasa Bogen (IEEE BigData, 2015)

How do information security workers use host data? A summary of interviews with security analysts. - Robert A. Bridges; Michael D. Iannacone; John R. Goodall; Justin M. Beaver (CoRR, 2018)

An Assessment of the Usability of Machine Learning Based Tools for the Security Operations Center. - Sean Oesch; Robert A. Bridges; Jared M. Smith; Justin M. Beaver; John R. Goodall; Kelly M. T. Huffer; Craig Miles; Daniel Scofield (iThings/GreenCom/CPSCom/SmartData/Cybermatics, 2020)

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection. - Robert A. Bridges; Sean Oesch; Miki E. Verma; Michael D. Iannacone; Kelly M. T. Huffer; Brian Jewell; Jeffrey A. Nichols; Brian Weber; Justin M. Beaver; Jared M. Smith; Daniel Scofield; Craig Miles; et al. (CoRR, 2020)

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors. - Robert A. Bridges; Brian Weber; Justin M. Beaver; Jared M. Smith; Miki E. Verma; Savannah Norem; Kevin D. Spakes; Cory L. Watson; Jeffrey A. Nichols; Brian Jewell; Michael D. Iannacone; Chelsey Dunivan Stahl; et al. (IEEE Big Data, 2023)

Beyond the Hype: An Evaluation of Commercially Available Machine Learning-based Malware Detectors. - Robert A. Bridges; Sean Oesch; Michael D. Iannacone; Kelly M. T. Huffer; Brian Jewell; Jeffrey A. Nichols; Brian Weber; Miki E. Verma; Daniel Scofield; Craig Miles; Thomas Plummer; Mark Daniell; et al. (DTRAP, 2023)