Statistical detection of format dialects using the weighted Dowker complex

This paper provides an experimentally validated, probabilistic model of file behavior when consumed by a set of pre-existing parsers. File behavior is measured by way of a standardized set of Boolean "messages" produced as the files are read. By thresholding the posterior probability that a file exhibiting a particular set of messages is from a particular dialect, our model yields a practical classification algorithm for two dialects. We demonstrate that this thresholding algorithm for two dialects can be bootstrapped from a training set consisting primarily of one dialect. Both the theoretical and the empirical distributions of file behaviors for one dialect yield good classification performance, and outperform classification based on simply counting messages.Our theoretical framework relies on statistical independence of messages within each dialect. Violations of this assumption are detectable and allow a format analyst to identify "boundaries" between dialects. By restricting their attention to the files that lie within these boundaries, format analysts can more efficiently craft new criteria for dialect detection.