Classifying Android Applications Via System Stats

Mobile devices and social media have been used to create empowering healthcare services. However, privacy and security concerns remain. Furthermore, the integration of interoperability biomedical standards is a strategic feature. Thus, the objective of this paper is to build enhanced healthcare services by merging all these components. Methodologically, the current mobile health telemonitoring architectures and their limitations are described, leading to the identification of new potentialities for a novel architecture. As a result, a standardized, secure/private, social-media-based mobile health architecture has been proposed and discussed. Additionally, a technical proof-of-concept (two Android applications) has been developed by selecting a social media (Twitter), a security envelope (open Pretty Good Privacy (openPGP)), a standard (Health Level 7 (HL7)) and an information-embedding algorithm (modifying the transparency channel, with two versions). The tests performed included a small-scale and a boundary scenario. For the former, two sizes of images were tested; for the latter, the two versions of the embedding algorithm were tested. The results show that the system is fast enough (less than 1 s) for most mHealth telemonitoring services. The architecture provides users with friendly (images shared via social media), straightforward (fast and inexpensive), secure/private and interoperable mHealth services.

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Compositional Taint Analysis of Native Codes for Security Vetting of Android Applications

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke50421.2020.9303643 ◽

2020 ◽

Author(s):

Seyed Behnam Andarzian ◽

Behrouz Tork Ladani

Keyword(s):

Taint Analysis ◽

Android Applications

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Detecting Colluding Inter-App Communication in Mobile Environment

Applied Sciences ◽

10.3390/app10238351 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8351

Author(s):

Rosangela Casolare ◽

Fabio Martinelli ◽

Francesco Mercaldo ◽

Antonella Santone

Keyword(s):

Model Checking ◽

Mobile Devices ◽

Private Information ◽

End Users ◽

Home Automation ◽

Mobile Environment ◽

Data Support ◽

Bank Transfer ◽

Android Applications

The increase in computing capabilities of mobile devices has, in the last few years, made possible a plethora of complex operations performed from smartphones and tablets end users, for instance, from a bank transfer to the full management of home automation. Clearly, in this context, the detection of malicious applications is a critical and challenging task, especially considering that the user is often totally unaware of the behavior of the applications installed on their device. In this paper, we propose a method to detect inter-app communication i.e., a colluding communication between different applications with data support to silently exfiltrate sensitive and private information. We based the proposed method on model checking, by representing Android applications in terms of automata and by proposing a set of logic properties to reduce the number of comparisons and a set of logic properties automatically generated for detecting colluding applications. We evaluated the proposed method on a set of 1092 Android applications, including different colluding attacks, by obtaining an accuracy of 1, showing the effectiveness of the proposed method.

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A first look at Android applications in Google Play related to COVID-19

Empirical Software Engineering ◽

10.1007/s10664-021-09943-x ◽

2021 ◽

Vol 26 (4) ◽

Cited By ~ 1

Author(s):

Jordan Samhi ◽

Kevin Allix ◽

Tegawendé F. Bissyandé ◽

Jacques Klein

Keyword(s):

Mobile Apps ◽

Contact Tracing ◽

Location Tracking ◽

Response Effort ◽

Sensitive Data ◽

Android Apps ◽

Digital World ◽

Public Health Organizations ◽

Android Applications ◽

Google Play

AbstractDue to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

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Detecting Malicious Android Applications Based On API calls and Permissions Using Machine learning Algorithms

2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) ◽

10.1109/miucc52538.2021.9447594 ◽

2021 ◽

Author(s):

Seif ElDein Mohamed ◽

Mostafa Ashaf ◽

Amr Ehab ◽

Omar Shereef ◽

Haytham Metwaie ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Android Applications

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A Modified FlowDroid Based on Chi-Square Test of Permissions

Entropy ◽

10.3390/e23020174 ◽

2021 ◽

Vol 23 (2) ◽

pp. 174

Author(s):

Hongzhaoning Kang ◽

Gang Liu ◽

Zhengping Wu ◽

Yumin Tian ◽

Lizhi Zhang

Keyword(s):

Detection Efficiency ◽

False Alarms ◽

Chi Square ◽

Secure Storage ◽

Chi Square Test ◽

Complex Detection ◽

Android Applications ◽

Malicious Apps ◽

Flow Detection ◽

Static Detection

Android devices are currently widely used in many fields, such as automatic control, embedded systems, the Internet of Things and so on. At the same time, Android applications (apps) always use multiple permissions, and permissions can be abused by malicious apps that disclose users’ privacy or breach the secure storage of information. FlowDroid has been extensively studied as a novel and highly precise static taint analysis for Android applications. Aiming at the problem of complex detection and false alarms in FlowDroid, an improved static detection method based on feature permission and risk rating is proposed. Firstly, the Chi-square test is used to extract correlated permissions related to malicious apps, and mutual information is used to cluster the permissions to generate feature permission clusters. Secondly, risk calculation method based on permissions and combinations of permissions are proposed to identify dangerous data flows. Experiments show that this method can significantly improve detection efficiency while maintaining the accuracy of dangerous data flow detection.

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