Malware detection with quantitative data flow graphs
Tobias Wüchner, Martín Ochoa, Alexander Pretschner · 2014
We propose a novel behavioral malware detection approach based on a generic system-wide quantitative data flow model. We base our data flow analysis on the incremental construction of aggregated quantitative data flow graphs. These graphs represent communication between different system entities such as processes, sockets, files or system registries. We demonstrate the feasibility of our approach through a prototypical instantiation and implementation for the Windows operating system. Our experiments yield encouraging results: in our data set of samples from common malware families and popular non-malicious applications, our approach has a detection rate of 96% and a false positive rate of less than 1.6%. In comparison with closely related data flow based approaches, we achieve similar detection effectiveness with considerably better performance: an average full system analysis takes less than one second.