Biblio
Filters: Author is Fugate, Sunny [Clear All Filters]
The World (of CTF) is Not Enough Data: Lessons Learned from a Cyber Deception Experiment. 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). :346–353.
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2019. The human side of cyber is fundamentally important to understanding and improving cyber operations. With the exception of Capture the Flag (CTF) exercises, cyber testing and experimentation tends to ignore the human attacker. While traditional CTF events include a deeply rooted human component, they rarely aim to measure human performance, cognition, or psychology. We argue that CTF is not sufficient for measuring these aspects of the human; instead, we examine the value in performing red team behavioral and cognitive testing in a large-scale, controlled human-subject experiment. In this paper we describe the pros and cons of performing this type of experimentation and provide detailed exposition of the data collection and experimental controls used during a recent cyber deception experiment-the Tularosa Study. Finally, we will discuss lessons learned and how our experiences can inform best practices in future cyber operations studies of human behavior and cognition.
Learning from Super-mutants: Searching Post-apocalyptic Software Ecosystems for Novel Semantics-preserving Transforms. Proceedings of the Genetic and Evolutionary Computation Conference Companion. :1529–1536.
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2017. In light of recent advances in genetic-algorithm-driven automated program modification, our team has been actively exploring the art, engineering, and discovery of novel semantics-preserving transforms. While modern compilers represent some of the best ideas we have for automated program modification, current approaches represent only a small subset of the types of transforms which can be achieved. In the wilderness of post-apocalyptic software ecosystems of genetically-modified and mutant programs, there exist a broad array of potentially useful software mutations, including semantics-preserving transforms that may play an important role in future software design, development, and most importantly, evolution.