Touchscreen-based devices such as smartphones and tablets are gaining popularity, but their rich input capabilities pose new development and testing complications. To alleviate this problem, we ...present an approach and tool named Reran that permits record-and-replay for the Android smartphone platform. Existing GUI-level record-and-replay approaches are inadequate due to the expressiveness of the smartphone domain, in which applications support sophisticated GUI gestures, depend on inputs from a variety of sensors on the device, and have precise timing requirements among the various input events. We address these challenges by directly capturing the low-level event stream on the phone, which includes both GUI events and sensor events, and replaying it with microsecond accuracy. Moreover, Reran does not require access to app source code, perform any app rewriting, or perform any modifications to the virtual machine or Android platform. We demonstrate RERAN's applicability in a variety of scenarios, including (a) replaying 86 out of the Top-100 Android apps on Google Play; (b) reproducing bugs in popular apps, e.g., Firefox, Facebook, Quickoffice; and (c) fast-forwarding executions. We believe that our versatile approach can help both Android developers and researchers.
This work presents the design of an open educational low-cost (35 euros) modular and extendable mobile robot based on Android and Arduino, with Local Area Network (LAN) and Internet connection ...capabilities, to be used as an educational tool in labs and classrooms of information and communications technology (ICT) vocational training, or in engineering courses, as well as in e-learning or massive open online courses (MOOC) as an alternative or complementary to virtual labs. It is a first step introducing what we call ”BYOR: Bring Your Own Robot” education policy equivalent to ”BYOD: Bring your own devices” in computers’ world.
The purpose of this study is to develop a postsecondary option-based android application that is used by junior secondary students in determining plans for further study. Planning further studies ...from junior to senior high school and beyond, certainly requires a consideration. All preparations both externally (school, facilities, environment) and internally (academic ability, understanding of learning), and all of that include in postsecondary options. Postsecondary options are awareness the opportunity to obtain a career and develop experts using various abilities such as grouping careers, the ability to assess themselves, and many more. Optimizing postsecondary options can be applied in technology. The research method used is research and development, which develops Android applications based on postsecondary options in helping student's postsecondary option. In this application, it contains information about the requirements, curriculum, job opportunities in a number of options for further study of students after junior high school and beyond.
Serverless computing has become very popular in recent times which facilitates a greater abstraction in virtualization technology. With the rapid development in serverless computing, a new paradigm ...has been evolved to design and develop android applications. However, the android platform has become one of the most vulnerable targets due to the increase in the number of users and hence raises a strong security concern to develop an advanced security framework. In this paper, a serverless computing based intelligent malware detection framework, called CloudIntellMal is proposed to detect malware targeting android-based applications. The framework incorporates some popular key cloud services such as Amazon Web Services (AWS) Elastic Cloud Compute (EC2) and Simple Storage Service (S3) to store and pre-process the logs taken from the end user devices. An efficient feature extraction algorithm is designed that employs ‘Bag of n-grams’ to develop feature vectors. The framework runs the machine learning algorithm in the cloud infrastructure to learn malicious patterns. The decision model is loaded in EC2 at the time of detection phase to classify the monitored apps. A prototype of the approach has been developed and validated using Drebin dataset and results seem to be promising.
Mobile applications that facilitate interaction between people have grown in popularity and, as a result, the number of e-dating applications have expanded. In these types of applications, there is ...usually a trade-off between user privacy and safety. On one hand, users want to keep their data as private as possible, on the other hand, user identification forces accountability, which, hopefully, will foster the development of responsible behaviors and minimize abuses. The Bumble e-dating app has been growing in popularity and differs from other apps by giving women the power to make the first contact after a match. Their main goal is to prevent women’s harassment. In this work, we study the digital artifacts that can be found after the use of the Bumble app on Android devices. Despite applying many measures to ensure data protection, it was possible to obtain information that identifies users and exchanged messages. The data structure stored on the device is described, as well as the artifacts with forensic value for an investigation. Moreover, a script was created to parse and visualize the main forensic artifacts of the Bumble app.
Obfuscation techniques modify an app’s source (or machine) code in order to make it more difficult to analyze. This is typically applied to protect intellectual property in benign apps, or to hinder ...the process of extracting actionable information in the case malware. Since malware analysis often requires considerable resource investment, detecting the particular obfuscation technique used may contribute to apply the right analysis tools, thus leading to some savings.
In this paper, we propose AndrODet, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation. AndrODet leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner. We compare our results with a batch learning algorithm using a dataset of 34,962 apps from both malware and benign apps. Experimental results show that online learning approaches are not only able to compete with batch learning methods in terms of accuracy, but they also save significant amount of time and computational resources. Particularly, AndrODet achieves an accuracy of 92.02% for identifier renaming detection, 81.41% for string encryption detection, and 68.32% for control flow obfuscation detection, on average. Also, the overall accuracy of the system when apps might be obfuscated with more than one technique is around 80.66%.
•An online learning system to detect 3 types of obfuscation in Android applications.•ID-renaming detection module identifies obfuscated apps after observing few samples.•String encryption detection module improves its accuracy by observing few apps.•Control flow obfuscation detection module reaches a good accuracy from few seen apps.•The proposed system is compared with a batch-learning equivalent by time and memory.
Android is widely recognized as one of the leading mobile operating systems globally. As the popularity and usage of Android OS and third-party application stores continue to soar, the process of ...developing and publishing applications has become increasingly accessible. However, the absence of a robust filtering mechanism to ensure that applications only request appropriate and secure permissions poses a significant concern. While extensive research has been conducted on malware analysis, the realm of intrusive applications remains largely unexplored. The lack of defensive measures to promptly identify invasive applications tilts the balance in favor of malicious actors and developers who may embed intrusive behavior within their products. It is imperative to develop new monitoring tools and techniques that address these privacy gaps. In light of this, we propose a Continuous Threat Monitoring Framework (CTMF) designed to safeguard mobile users from intrusive apps both before and after installation. Our framework, implemented and evaluated in the Android environment, offers practical deployability without imposing excessive overhead. It fills the void by considering the changes occurring within an app while it remains on a user's device, setting it apart from existing anti-intrusiveness solutions primarily focusing on app installation.
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