Publications

Leger, Michelle A.; Darling, Michael C.; Jones, Stephen T.; Matzen, Laura E.; Stracuzzi, David J.; Wilson, Andrew T.; Bueno, Denis B.; Christentsen, Matthew C.; Ginaldi, Melissa J.; Hannasch, David A.; Heidbrink, Scott H.; Howell, Breannan C.; Leger, Chris; Reedy, Geoffrey E.; Rogers, Alisa N.; Williams, Jack A.

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.