Combination attack of android applications analysis scheme based on privacy leak

2016 ◽  
Author(s):  
Jieming Gu ◽  
Chengze Li ◽  
Dian Lei ◽  
Qi Li
Keyword(s):  
Analysis Scheme ◽  
Android Applications

scholarly journals Building Standardized and Secure Mobile Health Services Based on Social Media

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2208
Author(s):  
Jesús D. Trigo ◽  
Óscar J. Rubio ◽  
Miguel Martínez-Espronceda ◽  
Álvaro Alesanco ◽  
José García ◽  
...  
Keyword(s):  
Social Media ◽  
Mobile Devices ◽  
Mobile Health ◽  
Healthcare Services ◽  
Small Scale ◽  
Proof Of Concept ◽  
Privacy And Security ◽  
Health Level 7 ◽  
Information Embedding ◽  
Android Applications

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.

scholarly journals Text Cohesion in English Scientific Texts Written by Saudi Undergraduate Dentistry Students: A Multimodal Discourse Analysis of Textual and Logical Relations in Oral Biology Texts

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110321
Author(s):  
Hesham Suleiman Alyousef
Keyword(s):  
Discourse Analysis ◽  
Undergraduate Students ◽  
Meaning Making ◽  
Linguistic Knowledge ◽  
Bachelor Of Science ◽  
Lexical Cohesion ◽  
Multimodal Discourse Analysis ◽  
Multimodal Discourse ◽  
Cohesive Devices ◽  
Analysis Scheme

This qualitative study examined multimodal cohesive devices in English oral biology texts by eight high-achieving Saudi English-as-a-foreign-language students enrolled in a Bachelor of Science Dentistry program. A Systemic Functional Multimodal Discourse Analysis (SF-MDA) of the textual and logical cohesive devices in oral biology texts was conducted, employing Halliday and Hasan’s cohesion analysis scheme. The findings showed that students used varied cohesive devices: lexical cohesion, followed by reference and conjunctions. Although ellipsis was minimally employed in the oral biology texts, its discipline-specific uses emerged: the use of bullet points and numbered lists that facilitate recall. The SF-MDA of cohesion in multimodal semiotic resources highlighted the processes underlying construction of conceptual and linguistic knowledge of cohesive devices in oral biology texts. The results indicate that oral biology discourse is interdisciplinary, including a number of subfields in biology. The SF-MDA of pictorial oral biology representations indicates that they include instances of cohesive devices that illustrate and complement verbal texts. The results indicate that undergraduate students need to be provided with a variety of multimodal high-cohesion texts so that they can successfully extend underlying conceptual and logical meaning-making relations.

scholarly journals Detecting Colluding Inter-App Communication in Mobile Environment

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.

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

2021 ◽  
Vol 26 (4) ◽  
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.

scholarly journals A Modified FlowDroid Based on Chi-Square Test of Permissions

Entropy ◽  
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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