In this paper, we perform a comprehensive study of 2,470 patched Android vulnerabilities that we collect from different data sources such as Android security bulletins, CVEDetails, Qualcomm Code Aurora, AOSP Git repository, and Linux Patchwork. In our data analysis, we focus on determining the affected layers, OS versions, severity levels, and common weakness enumerations (CWE) associated with the patched vulnerabilities. Further, we assess the timeline of each vulnerability, including discovery and patch dates. We find that (i) even though the number of patched vulnerabilities changes considerably from month to month, the relative number of patched vulnerabilities for each severity level remains stable over time, (ii) there is a significant delay in patching vulnerabilities that originate from the Linux community or concern Qualcomm components, even though Linux and Qualcomm provide and release their own patches earlier, (iii) different AOSP versions receive security updates for different periods of time, (iv) for 94 of patched Android vulnerabilities, the date of disclosure in public datasets is not before the patch release date, (v) there exist some inconsistencies among public vulnerability data sources, e.g., some CVE IDs are listed in Android Security bulletins with detailed information, but in CVEDetails they are listed as unknown, (vi) many patched vulnerabilities for newer Android versions likely also affect older versions that do not receive security patches due to end-of-life.
Security vulnerabilities in servers and operating systems are software defects that represent great risks. Both software developers and users are struggling to contain the risk posed by these vulnerabilities. The vulnerabilities are discovered by both developers and external testers throughout the life-span of a software system. A few models for the vulnerability discovery process have just been published recently. Such models will allow effective resource allocation for patch development and are also needed for evaluating the risk of vulnerability exploitation. Here we examine these models for the vulnerability discovery process. The models are examined both analytically and using actual data on vulnerabilities discovered in three widely-used systems. The applicability of the proposed models and significance of the parameters involved are discussed. The limitations of the proposed models are examined and major research challenges are identified A key aspect of better and more secure software is timely patch release by software vendors for the vulnerabilities in their products. Software vulnerability disclosure, which refers to the publication of vulnerability information, has generated intense debate. An important consideration in this debate is the behavior of software vendors. How quickly do vendors patch vulnerabilities and how does disclosure affect patch release time? This paper compiles a unique data set from the Computer Emergency Response Team Coordination Center (CERT) and SecurityFocus to answer this question. Our results suggest that disclosure accelerates patch release. The instantaneous probability of releasing the patch rises by nearly two and a half times because of disclosure. Open source vendors release patches more quickly than closed source vendors. Vendors are more responsive to more severe vulnerabilities. We also find that vendors respond more slowly to vulnerabilities not disclosed by CERT. We verify our results by using another publicly available data set and find that results are consistent. We also show how our estimates can aid policy makers in their decision making. Vendors in the Android ecosystem typically customize their devices by modifying Android Open Source Project (AOSP) code, adding in-house developed proprietary software, and pre-installing third-party applications. However, research has documented how various security problems are associated with this customization process. We develop a model of the Android ecosystem utilizing the concepts of game theory and product differentiation to capture the competition involving two vendors customizing the AOSP platform. We show how the vendors are incentivized to differentiate their products from AOSP and from each other, and how prices are shaped through this differentiation process. We also consider two types of consumers: security-conscious consumers who understand and care about security, and na "ive consumers who lack the ability to correctly evaluate security properties of vendor-supplied Android products or simply ignore security. It is evident that vendors shirk on security investments in the latter case. Regulators such as the U.S. Federal Trade Commission have sanctioned Android vendors for underinvestment in security, but the exact effects of these sanctions are difficult to disentangle with empirical data. Here, we model the impact of a regulator-imposed fine that incentivizes vendors to match a minimum security standard. Interestingly, we show how product prices will decrease for the same cost of customization in the presence of a fine, or a higher level of regulator-imposed minimum security. 