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There is little information from independent sources in the public domainabout mobile malware infection rates. The only previous independent estimate(0.0009%) [12], was based on indirect measurements obtained from domain nameresolution traces. In this paper, we present the first independent study ofmalware infection rates and associated risk factors using data collecteddirectly from over 55,000 Android devices. We find that the malware infectionrates in Android devices estimated using two malware datasets (0.28% and0.26%), though small, are significantly higher than the previous independentestimate. Using our datasets, we investigate how indicators extractedinexpensively from the devices correlate with malware infection. Based on thehypothesis that some application stores have a greater density of maliciousapplications and that advertising within applications and cross-promotionaldeals may act as infection vectors, we investigate whether the set ofapplications used on a device can serve as an indicator for infection of thatdevice. Our analysis indicates that this alone is not an accurate indicator forpinpointing infection. However, it is a very inexpensive but surprisinglyuseful way for significantly narrowing down the pool of devices on whichexpensive monitoring and analysis mechanisms must be deployed. Using our twomalware datasets we show that this indicator performs 4.8 and 4.6 times(respectively) better at identifying infected devices than the baseline ofrandom checks. Such indicators can be used, for example, in the search for newor previously undetected malware. It is therefore a technique that cancomplement standard malware scanning by anti-malware tools. Our analysis alsodemonstrates a marginally significant difference in battery use betweeninfected and clean devices.

The Company You Keep: Mobile Malware Infection Rates and Inexpensive Risk Indicators