Accurate Path Prediction of Provenance Traces

Several security and workflow applications require provenance information at the operating system level for diagnostics. The resulting provenance traces are often more informative if they are efficiently mapped to execution paths within the control flow graph. However, current provenance systems do not map traces to control flow graphs for diagnostics purposes due to the computational complexity of mapping traces to graphs. We formulate the path prediction problem for provenance traces and take a machine learning approach to solve the problem. We develop a transformer-based graph convolutional network to predict paths. Our experiments demonstrate that our machine learning model achieves more than twice the accuracy on average compared to simple probabilistic models, with an increased computation time trade-off.