Abstract
There is generally a lack of consensus in Antivirus (AV) engines’ decisions on a given sample. This challenges the building of authoritative ground-truth datasets. Instead, researchers and practitioners may rely on unvalidated approaches to build their ground truth, e.g., by considering decisions from a selected set of Antivirus vendors or by setting up a threshold number of positive detections before classifying a sample. Both approaches are biased as they implicitly either decide on ranking AV products, or they consider that all AV decisions have equal weights. In this paper, we extensively investigate the lack of agreement among AV engines. To that end, we propose a set of metrics that quantitatively describe the different dimensions of this lack of consensus. We show how our metrics can bring important insights by using the detection results of 66 AV products on 2 million Android apps as a case study. Our analysis focuses not only on AV binary decision but also on the notoriously hard problem of labels that AVs associate with suspicious files, and allows to highlight biases hidden in the collection of a malware ground truth—a foundation stone of any malware detection approach.
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Notes
- 1.
We make available a full open source implementation under the name STASE at https://github.com/freaxmind/STASE.
- 2.
- 3.
Since the goal of this study is not to evaluate the individual performance of antivirus engines, their names have been omitted and replaced by an unique number (ID).
- 4.
We could not obtain the results for 54 151 (2.56 %) applications because of a file size limit by VirusTotal.
- 5.
AVs considered in [8]: AntiVir, AVG, Bit- Defender, ClamAV, ESET, F-Secure, Kaspersky, McAfee, Panda, Sophos.
- 6.
If one set of AVs leads to a percentage x over 50 %, then the other set relevant value is 100-x% < 50 %.
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Acknowledgment
This work was supported by the Fonds National de la Recherche (FNR), Luxembourg, under the project AndroMap C13/IS/5921289.
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Hurier, M., Allix, K., Bissyandé, T.F., Klein, J., Le Traon, Y. (2016). On the Lack of Consensus in Anti-Virus Decisions: Metrics and Insights on Building Ground Truths of Android Malware. In: Caballero, J., Zurutuza, U., Rodríguez, R. (eds) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2016. Lecture Notes in Computer Science(), vol 9721. Springer, Cham. https://doi.org/10.1007/978-3-319-40667-1_8
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