Publications

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This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

Understanding the data structures employed by a program is important for reverse engineering activities and can improve the results of automated software analysis techniques. In a compiled binary, access to data structure fields and array indices defined in the source program are replaced by raw pointer arithmetic. We present a representation for capturing the essential details of how a program accesses memory regions, which we call a Memory Access Graph (MAG), and a static analysis for automatically extracting this information from a program binary. The static analysis to extract the MAGs from the program is straightforward and does not require sophisticated integer or pointer analysis. The MAGs are readily understood by reverse engineers; they are generally able to perceive the data structure definition corresponding to a MAG. We briefly discuss automatic extraction of structure definitions outlining some of the difficulties in doing so.

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