With the rapid growth of developing malwares in android platform as the widest used mobile operating system, knowing security risk of an application (app) can be helpful for warning users regarding the use of potential malicious applications. The security risk of an android app can be estimated using its requested permissions. In this paper, the concept of critical permissions is precisely re-defined according to the abuse of permissions by previously known android malwares. Based on this definition and analysis of requested permissions of the large numbers of malwares and benign apps, a new criterion is proposed to measure the security risk of the apps. In this criterion, informative permissions have higher impact on the resulting measured security risk values of the apps. Experimental evaluations show the superiority of the proposed criterion with respect to previously proposed ones in terms of detection rete and generalization capability.

Most of the current research on static analysis of android applications for security vetting either works on Java source code or the Dalvik bytecode. Nevertheless, android allows developers to use C or C++ code in their programs compiled into various binary architectures. Moreover, Java and the native code components (C or C++) can collaborate using the Java Native Interface. Recent research shows that native codes are frequently used in both benign and malicious android applications. Most of the present android static analysis tools avert considering native codes in their analysis and applied trivial models for their data-flow analysis. As we know, only the open-source JN-SAF tool has tried to solve this issue statically. However, there are still challenges like libC functions and multi-threading in native codes that we want to address in this work. We presented SANT as an extension of JN-SAF for supporting Static Analysis of Native Threads. We considered modeling libC functions in our data-flow analysis to have a more precise analysis when dealing with security vetting of native codes. We also used control flow and data dependence graphs in SANT to handle multiple concurrent threads and find implicit data-flow between them. Our experiments show that the conducted improvements outperform JN-SAF in real-world benchmark applications.

Purpose: Food security is a critical global challenge that is influenced by research and innovation in the field. Therefore, the objective of this study is to analyze the scientific output of developing countries in food security and examine its relationship with patents and Gross Domestic Product (GDP).Methodology: This applied research utilized the Scientometric approach. A total of 8,416 papers published between 1992-2023 in the field of food security by developing countries were included in the study using citation databases from Clarivate Analytics. Additionally, patent registrations from the WIPO database and GDP data from the World Bank were analyzed. Information was collected through note-taking, and the data was analyzed using Pearson's correlation coefficient.Findings: The findings reveal an upward trend in the publication and citation of scientific outputs related to food security in developing countries. China has higher numbers of papers, patents, GDP, and food production index compared to Iran, Japan, and South Korea. There is also a positive correlation observed between population and the number of papers, gross production and the number of papers, food production and the number of published papers, as well as the number of patents and papers citing scientific outputs of countries.Conclusion: These results highlight the significant relationship between increasing scientific output, GDP, the number of patents, and food security. Greater emphasis on food security contributes to enhanced scientific output, GDP, and innovation. Similarly, increasing scientific output, GDP, and innovation positively impact food security in countries.Value: This study emphasizes the importance of scientific outputs in driving technological advancements, innovations, and ultimately, ensuring food security in developing countries.

In this paper, we consider to seek vulnerabilities and we conduct possible attacks on the crucial and essential parts of android OSs architecture including the framework and the android kernel layers. As a regard, we explain the Binder component of android OS from security point of view. Then, we demonstrate how to penetrate into the Binder and control data exchange mechanism in android OS by proposing a kernel level attack model based on the hooking method. In addition, we provide a method to detect these kinds of attacks on android frameworks and the kernel layer. As a result, by implementing the attack model, it is illustrated that the android processes are detectable and the data can be extracted from any process and system calls. On the other hand, by using our detection proposed method the possibility of using this attack approach in the installed applications on the android smartphones will be sharply decreased.

In software programs, most of the time, there is a chance for occurrence of faults in general, and exception faults in particular. Localizing those pieces of code that are responsible for a particular fault is one of the most complicated tasks, and it can produce incorrect results if done manually. Semi-automated and fully-automated techniques have been introduced to overcome this issue. However, despite recent advances in fault localization techniques, they are not necessarily applicable to android applications because of their special characteristics such as context-awareness, use of sensors, being executable on various mobile devices, limited hardware resources, etc. To this aim, in this paper, a semi-automated hybrid method is introduced that combines static and dynamic analyses to localize exception faults in android applications. Our evaluations of nine open source android applications of di erent sizes with various exceptions show that the technique proposed in this paper can correctly identify root causes of the occurred exceptions. These results indicate that our proposed approach is e ective in practice in localizing exception faults in android applications.

android is a widely used operating system that employs a permission-based access control model. The android Permissions System (APS) is responsible for mediating application resource requests. APS is a critical component of the android security mechanism,hence, a failure in the design of APS can potentially lead to vulnerabilities that grant unauthorized access to resources by malicious applications. In this paper, we present a formal approach for modeling and verifying the security properties of APS. We demonstrate the usability of the proposed approach by showcasing the detection of a well-known vulnerability found in android’, s custom permissions.

With the increase in the Internet's penetration rate in life and the use of this technology in all aspects, the use of mobile phones has increased as well. This, in addition to creating many benefits, has expanded and accelerated the release of some malicious programs called malware. In this study, it is attempted to use a multilayer neural network and learning machine diagnosis of zero daytime malware on smartphones. For this purpose, the standard database has been labeled with more than 15,000 samples of malware and goodware. In the pre -processing phase, the data is first performed using normalization and alignment of the data and by analyzing the main components of the feature of the selection of the feature and selected from 1183 features 215 features that have higher variances, followed by the model. A suggestion is introduced from the multilayer neural network class and the optimization algorithm based on the training and learning that apply it to the databases and compare its classification results with vector algorithms, genetic algorithm, nearest neighbor. And ... it can be seen that the neural network training increases accuracy and accuracy. The results of the use of multilayer neural network based on education and learning indicate 99% accuracy and 98% accuracy.

Aims: Breast cancer is among the most common cancer types. This study aimed to develop an android-based detection application for assessing breast cancer risk. Materials & Methods: This quasi-experimental study utilized a research and development approach, employing a pre- and post-test design with one group. The development and field-testing phase took place between July and August 2023, involving 59 women of childbearing age purposively selected within the operational vicinity of Puskesmas Simpang IV Sipin, Jambi City, Indonesia. The application successfully underwent testing, including evaluation by media and material validators. Subsequently, a comprehension test was conducted with three respondents individually, ten individuals in a small group, and 59 participants in a large group. In the field test, data are presented descriptively, including frequencies. The Wilcoxon test was utilized to determine a causal relationship between the product’s usage and the observed impact. Findings: The development of the breast cancer risk assessment application involved several key stages, including the identification stage (comprising problem analysis, context, and literature), the application model design stage, and the material and media validation stage. The media validation process was conducted twice, with the findings yielding a score of 67, averaging 3.35 (meeting valid criteria), while material validation received an average score of 3.0 (also meeting valid criteria). A Wilcoxon test conducted on the knowledge variable revealed a significant increase, with the mean value before the intervention at 8.44 and post-intervention rising to 12.29. Conclusion: Women of childbearing age readily accept android-based breast cancer risk detection applications, and their usage has a positive impact on increasing their knowledge.