We study the temporal dynamics of potentially harmful apps (PHAs) on Android by leveraging 8.8M daily on-device detections collected among 11.7M customers of a popular mobile security product between 2019 and 2020. We show that the current security model of Android, which limits security products to run as regular apps and prevents them from automatically removing malicious apps opens a significant window of opportunity for attackers. Such apps warn users about the newly discovered threats, but users do not promptly act on this information, allowing PHAs to persist on their device for an average of 24 days after they are detected. We also find that while app markets remove PHAs after these become known, there is a significant delay between when PHAs are identified and when they are removed: PHAs persist on Google Play for 77 days on average and 34 days on third party marketplaces. Finally, we find evidence of PHAs migrating to other marketplaces after being removed on the original one. This paper provides an unprecedented view of the Android PHA landscape, showing that current defenses against PHAs on Android are not as effective as commonly thought, and identifying multiple research directions that the security community should pursue, from orchestrating more effective PHA takedowns to devising better alerts for mobile security products.
Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. There are hundreds of such security apps, both free and paid in Google Play Store and some of them are downloaded millions of times. By installing security apps, the smartphone users place a significant amount of trust towards the security companies who developed these apps, because a fully functional mobile security app requires access to many smartphone resources such as the storage, text messages and email, browser history, and information a…
The Android Open Source Project (AOSP) is probably the most used and customized operating system for smartphones and IoT devices worldwide. Its market share and high adaptability makes Android an interesting operating system for many developers. Nowadays, we use Android firmware in smartphones, TVs, smartwatches, cars, and other devices by various vendors and manufacturers. The sheer amount of customized Android firmware and devices makes it hard for security analysts to detect potentially harmful applications. Another fact is that many vendors include apps from 3rd party developers. Such bloa…
智能手机技术的最新发展使其应用市场变得巨大而不安全。每天都有大量的应用程序在android market（主要是在google play store上）推出，没有任何特别的检查，这造成了很多安全问题，而且这些问题仍然没有得到解决。有很多最近和日益增加的安全问题，主要是由Android应用程序引起的。主要是，通过应用程序访问用户数据依赖于用户对应用程序的权限。因此，Android智能手机上的不同应用程序如何绕过Android权限模型获得root访问权限以及用户数据如何被其应用程序泄露或窃取一直是一个大问题。这并不令人惊讶，谷歌Play商店有大量的恶意应用程序，可能会引起用户的注意，成为受害者之一，但有时它可能比我们想象的更糟糕。在本文中，我们将看看安全问题（即生根手机，恶意软件绕过和辅存）和如何容易获得用户的手机通过使用其应用程序.此外，我将讨论Android更新机制及其功能，以及可能的第三方绕过和根访问的漏洞.此外，一个关键的功能和来源的所有可能的访问路由到android应用程序我将讨论一个android权限模型。最后，我将首次提出基于特征的开发模型的概念，实现基于特征的权限选择作为Android应用系统的解决方案，并详细描述了基于特征的权限模型是如何解决当前使用Android权限模型的有意（恶意编码）或无意（编码不良）编码所导致的安全问题的。
While extremely valuable to achieve advanced functions, mobile phone sensors can be abused by attackers to implement malicious activities in Android apps, as experimentally demonstrated by many state-of-the-art studies. There is hence a strong need to regulate the usage of mobile sensors so as to keep them from being exploited by malicious attackers. However, despite the fact that various efforts have been put in achieving this, i.e., detecting privacy leaks in Android apps, we have not yet found approaches to automatically detect sensor leaks in Android apps. To fill the gap, we designed and …
Malware authors reuse the same program segments found in other applications for performing the similar kind of malicious activities such as information stealing, sending SMS and so on. Hence, there may exist several semantically similar malware samples in a family/dataset. Many researchers unaware about these semantically similar apps and use their features in their ML models for evaluation. Hence, the performance measures might be seriously affected by these similar kinds of apps. In this paper, we study the impact of semantically similar applications in the performance measures of ML based A…
The Android unrestricted application market, being of open source nature, has made it a popular platform for third-party applications reaching millions of smart devices in the world. This tremendous increase in applications with an extensive API that includes access to phone hardware, settings, and user data raises concerns regarding users privacy, as the information collected from the apps could be used for profiling purposes. In this respect, this paper focuses on the geolocation data and analyses five GPS applications to identify the privacy risks if no appropriate safeguards are present. O…
The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However, there is no official documentation providing the complete list of permission-protected APIs and the corresponding permissions to date. Researchers have spent significant efforts extracting such API protection mapping from the Android API framework, which leverages static code analysis to determine if specific permissions are required before accessing an API. Nevertheless, none of them has attempted to analyze the pro…
DNS over TLS (DoT) and DNS over HTTPS (DoH) promise to improve privacy and security of DNS by encrypting DNS messages, especially when messages are padded to a uniform size. Firstly, to demonstrate the limitations of recommended padding approaches, we present Segram, a novel app fingerprinting attack that allows adversaries to infer which mobile apps are executed on a device. Secondly, we record traffic traces of 118 Android apps using 10 different DoT/DoH resolvers to study the effectiveness of Segram under different conditions. According to our results, Segram identifies apps with accuracies…
As privacy features in Android operating system improve, privacy-invasive apps may gradually shift their focus to non-standard and covert channels for leaking private user/device information. Such leaks also remain largely undetected by state-of-the-art privacy analysis tools, which are very effective in uncovering privacy exposures via regular HTTP and HTTPS channels. In this study, we design and implement, ThirdEye, to significantly extend the visibility of current privacy analysis tools, in terms of the exposures that happen across various non-standard and covert channels, i.e., via any pro…
Android is nowadays the most popular operating system in the world, not only in the realm of mobile devices, but also when considering desktop and laptop computers. Such a popularity makes it an attractive target for security attacks, also due to the sensitive information often manipulated by mobile apps. The latter are going through a transition in which the Android ecosystem is moving from the usage of Java as the official language for developing apps, to the adoption of Kotlin as the first choice supported by Google. While previous studies have partially studied security weaknesses affectin…
Contemporary mobile applications (apps) are designed to track, use, and share users' data, often without their consent, which results in potential privacy and transparency issues. To investigate whether mobile apps have always been (non-)transparent regarding how they collect information about users, we perform a longitudinal analysis of the historical versions of 268 Android apps. These apps comprise 5,240 app releases or versions between 2008 and 2016. We detect inconsistencies between apps' behaviors and the stated use of data collection in privacy policies to reveal compliance issues. We u…
Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produce security-centric descriptions by performing program analysis. However, the generated text does not always address users' concerns as they are generally too technical to be understood by ordinary users. Moreover, different users have varied linguistic preferences, which do not match the text. Motivated by this challenge, we develop an innovative scheme to help users avoid malware and privacy-breaching apps by gener…
Contact tracing has historically been used to retard the spread of infectious diseases, but if it is exercised by hand in large-scale, it is known to be a resource-intensive and quite deficient process. Nowadays, digital contact tracing has promptly emerged as an indispensable asset in the global fight against the coronavirus pandemic. The work at hand offers a meticulous study of all the official Android contact tracing apps deployed hitherto by European countries. Each app is closely scrutinized both statically and dynamically by means of dynamic instrumentation. Depending on the level of ex…
Numerous studies demonstrated that browser fingerprinting is detrimental to users' security and privacy. However, little is known about the effects of browser fingerprinting on Android hybrid apps -- where a stripped-down Chromium browser is integrated into an app. These apps expand the attack surface by employing two-way communication between native apps and the web. This paper studies the impact of browser fingerprinting on these embedded browsers. To this end, we instrument the Android framework to record and extract information leveraged for fingerprinting. We study over 20,000 apps, inclu…
With the releases of Android Oreo and Pie, Android introduced some background execution limitations for apps. Google restricted the execution of background services to save energy and to prevent apps from running endlessly in the background. Moreover, access to the device's sensors was changed and a new concept named foreground service has been introduced. Apps were no longer allowed to run background services in an idle state, preventing apps from using the device's resources like the camera. These limitations, however, would not affect so-called foreground services because they show a perman…
Famous for its superior performance, deep learning (DL) has been popularly used within many applications, which also at the same time attracts various threats to the models. One primary threat is from adversarial attacks. Researchers have intensively studied this threat for several years and proposed dozens of approaches to create adversarial examples (AEs). But most of the approaches are only evaluated on limited models and datasets (e.g., MNIST, CIFAR-10). Thus, the effectiveness of attacking real-world DL models is not quite clear. In this paper, we perform the first systematic study of adv…
The existence of native code in Android apps plays an important role in triggering inconspicuous propagation of secrets and circumventing malware detection. However, the state-of-the-art information-flow analysis tools for Android apps all have limited capabilities of analyzing native code. Due to the complexity of binary-level static analysis, most static analyzers choose to build conservative models for a selected portion of native code. Though the recent inter-language analysis improves the capability of tracking information flow in native code, it is still far from attaining similar effect…
Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app installations typically happen via the official and alternative markets, but also via other smaller and less understood alternative distribution vectors such as Web downloads, pay-per-install (PPI) services, backup restoration, bloatware, and IM tools. This work performs a thorough investigation on unwanted app distribution by quantifying and comparing distribution t…
Android apps include third-party native libraries to increase performance and to reuse functionality. Native code is directly executed from apps through the Java Native Interface or the Android Native Development Kit. Android developers add precompiled native libraries to their projects, enabling their use. Unfortunately, developers often struggle or simply neglect to update these libraries in a timely manner. This results in the continuous use of outdated native libraries with unpatched security vulnerabilities years after patches became available. To further understand such phenomena, we stu…
Evaluating LLMs for Obfuscation Detection and Classification in Android Apps
IMT School for Advanced Studies Lucca（卢卡高等研究学院）；Interdisciplinary Centre for Security, Reliability and Trust（安全、可靠性和信任跨学科中心）；University of Verona（维罗纳大学）；University of Genoa（热那亚大学）
Android virtualization enables an app to create a virtual environment, in which other apps can run. Originally designed to overcome the limitations of mobile apps dimensions, malicious developers soon started exploiting this technique to design novel attacks. As a consequence, researchers proposed new defence mechanisms that enable apps to detect whether they are running in a virtual environment. In this paper, we propose Mascara, the first attack that exploits the virtualization technique in a new way, achieving the full feasibility against any Android app and proving the ineffectiveness of e…
Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application's compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps' API calls. To successfully analyze a modern d…
While many studies have looked at privacy properties of the Android and Google Play app ecosystem, comparatively much less is known about iOS and the Apple App Store, the most widely used ecosystem in the US. At the same time, there is increasing competition around privacy between these smartphone operating system providers. In this paper, we present a study of 24k Android and iOS apps from 2020 along several dimensions relating to user privacy. We find that third-party tracking and the sharing of unique user identifiers was widespread in apps from both ecosystems, even in apps aimed at childr…
