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The modern software development landscape has seen a shift in focus toward mobile applications as tablets and smartphones near ubiquitous adoption. Due to this trend, the complexity of these apps has been increasing, making development and…
With the Increasing use of Machine Learning in Android applications, more research and efforts are being put into developing better-performing machine learning algorithms with a vast amount of data. Along with machine learning for mobile…
Privacy concerns have long been expressed around smart devices, and the concerns around Android apps have been studied by many past works. Over the past 10 years, we have crawled and scraped data for almost 1.9 million apps, and also stored…
Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns,…
Context: Post-release user feedback plays an integral role in improving software quality and informing new features. Given its growing importance, feedback concerning security enhancements is particularly noteworthy. In considering the…
Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A…
As mobile app usage continues to rise, so does the generation of extensive user interaction data, which includes actions such as swiping, zooming, or the time spent on a screen. Apps often collect a large amount of this data and claim to…
We have identified a methodological problem in the empirical evaluation of the string encryption detection capabilities of the AndrODet system described by Mirzaei et al. in the recent paper "AndrODet: An adaptive Android obfuscation…
Researchers and commercial companies have made a lot of efforts on detecting malware in Android platform. However, a recent malware threat, App collusion, makes malware detection challenging. In App collusion, two or more Apps collaborate…
The Android framework provides a rich set of APIs that can be exploited by developers to build their apps. However, the rapid evolution of these APIs jointly with the specific characteristics of the lifecycle of the Android components…
Third-party Software Development Kits (SDKs) are widely adopted in Android app development, to effortlessly accelerate development pipelines and enhance app functionality. However, this convenience raises substantial concerns about…
Several solutions ensuring the dynamic detection of malicious activities on Android ecosystem have been proposed. These are represented by generic rules and models that identify any purported malicious behavior. However, the approaches…
Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream…
Sensing on smartphones is known to be power-hungry. It has been shown that this problem can be solved by adding an ultra low-power processor to execute simple, frequent sensor data processing. While very effective in saving energy, this…
Although iOS is the second most popular mobile operating system and is often considered the more secure one, approaches to automatically analyze iOS applications are scarce and generic app analysis frameworks do not exist. This is on the…
With the continuous growth in the usage of Android apps, ensuring their security has become critically important. An increasing number of malicious apps adopt anti-analysis techniques to evade security measures. Although some research has…
Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to end users, many deep learning models are now embedded in…
Privacy, security, and accessibility, like ethical concerns in mobile applications (a.k.a. apps), commonly subsumed under non-functional requirements, are generally reported by users through app reviews available in app stores. However,…
For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the…
Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big concern. As the number of new Android applications tends to be…