Dr. Ann Marie Reinhold whose paper received the Best Paper Award for Women in Cyber Security at the premiere IEEE Cyber Security & Resilience Conference hosted in Venice, Italy. The paper entitled New Version, New Answer: Investigating Cybersecurity Static-Analysis Tool Findings utilizes a data science approach to systematically investigate challenges inherent in the automated detection of weaknesses and vulnerabilities in compiled code. Reinhold led the research team, which included the mentoring of two undergraduate students, Travis Weber and Coleen Lemak; the team also included Montana State University professor, Dr. Clemente Izurieta, and postdoctoral scholar, Dr. Derek Reimanis. The award was sponsored by the Italy chapter of Women4Cyber, the European Cyber Security Organization whose objective is "to promote, encourage and support the participation of women in the field of cybersecurity." Reinhold is the co-director of the Software Engineering and Cyber Security Lab and the primary advisor for four women seeking graduate degrees in the Gianforte School of Computing at Montana State University.

Dr. Matthew Revelle and Dr. Fangtian Zhong  will be joining the Software Engineering and Cybersecurity Lab.

Welcome, Dr. Revelle and Dr. Zhong! Learn more about Dr. Revelle and Dr. Zhong on the Teams tab.

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Looking for funding? This may be for you:

The Cybersecurity Lab at Montana State University is searching for several PhD students interested in developing novel techniques in program analysis, formal methods, and/or machine learning for automating portions of the vulnerability discovery process and reasoning over vulnerabilities in computer systems. These positions are fully funded, tuition waivers and a research stipend are provided, and students will work with Dr. Matt Revelle and other members of the lab.

Familiarity with some of the following topics is relevant but not required: software vulnerabilities, vulnerability research, reverse engineering, emulation, data-flow analysis, type systems, constraint solvers, symbolic execution, abstract interpretation, graph neural networks, and large language models. This position will support learning any of these topics and others needed to advance the state of the art. If you are interested in this research area, please do apply, regardless of your prior experience with these topics.

Current and prospective students interested in this research area are encouraged to contact Dr. Matt Revelle for additional information. Over the summer, Dr. Revelle's email address is [email protected].