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

机译：您的公司：移动恶意软件感染率和廉价的风险指标

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There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%), was based on indirect measurements obtained from domain-name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that, while not an accurate indicator of infection by itself, the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed. Using our two malware datasets we show that this indicator performs up to about five times better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices.
机译：来自公共领域的独立来源的信息很少，涉及移动恶意软件的感染率。之前唯一的独立估计（0.0009％）是基于从域名解析记录中获得的间接测量结果。在本文中，我们使用从55,000多个Android设备直接收集的数据，提出了对恶意软件感染率和相关风险因素的首次独立研究。我们发现，使用两个恶意软件数据集（分别为0.28％和0.26％）估算出的Android设备中的恶意软件感染率虽然很小，但明显高于之前的独立估算。基于这样的假设，即某些应用程序商店具有更高的恶意应用程序密度，并且在应用程序内进行广告和交叉促销交易可能会成为感染媒介，因此，我们调查了设备上使用的一组应用程序是否可以用作感染的指标。该设备。我们的分析表明，虽然应用程序集本身并不是准确的感染指示，但它确实是一种便宜的方法，可用于标识应在其上部署更昂贵的监视和分析机制的设备池。使用我们的两个恶意软件数据集，我们表明，该指标在识别受感染设备方面的性能比随机检查的基准性能高约五倍。例如，可以在搜索新的或以前未检测到的恶意软件时使用此类指示符。因此，它是一种可以补充标准恶意软件扫描的技术。我们的分析还表明，受感染的设备和干净的设备之间在电池使用方面存在显着差异。

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