KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Online Publication Date2017-12-04
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/626759
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AbstractEffective malware detection approaches need not only high accuracy, but also need to be robust to changes in the modus operandi of criminals. In this paper, we propose Marmite, a feature-Agnostic system that aims at propagating known malicious reputation of certain files to unknown ones with the goal of detecting malware. Marmite does this by looking at a graph that encapsulates a comprehensive view of how files are downloaded (by which hosts and from which servers) on a global scale. The reputation of files is then propagated across the graph using semi-supervised label propagation with Bayesian confidence. We show that Marmite is able to reach high accuracy (0.94 G-mean on average) over a 10-day dataset of 200 million download events. We also demonstrate that Marmite's detection capabilities do not significantly degrade over time, by testing our system on a 30-day dataset of 660 million download events collected six months after the system was tuned and validated. Marmite still maintains a similar accuracy after this period of time.
CitationStringhini G, Shen Y, Han Y, Zhang X (2017) Marmite. Proceedings of the 33rd Annual Computer Security Applications Conference on - ACSAC 2017. Available: http://dx.doi.org/10.1145/3134600.3134604.
SponsorsWe would like to thank the anonymous reviewers for their feedback, and our shepherd Christian Rossow for his help in improving the final version of this paper. This work was supported by UCL through a BEAMS Future Leaders in Engineering and Physical Sciences Award and by the EPSRC under grant EP/N008448/1.
Conference/Event name33rd Annual Computer Security Applications Conference, ACSAC 2017