Android Operating System Research Articles

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

The influence of blue light sources on somnological disorders among patients with mild to moderate acne

Background. The acne is a chronic inflammatory skin disease that affects all age groups of the population and has a serious impact on the quality of life of patients. Various endogenous and exogenous factors are involved in the pathogenesis of dermatosis. The increasing of usage of digital media and the Internet has an impact on people's sleep habits and, with changes in sleep quality, symptoms of depression, anxiety and stress may increase, which leads to a more severe course of chronic dermatoses, including acne. Aim. To evaluate the relationship between somnological disorders (SLD) and the frequency of smartphone use before bedtime in patients with mild to moderate acne (MMA). Materials and methods. A single-center observational prospective study was conducted from 2023 to 2024: 130 patients aged 18 to 30 years (average age 23.32±2.75 years) diagnosed with MMA were observed. The data on the severity of acne were assessed using the Global Acne Grading System (GAGS), the degree of sleep disturbance was determined using the Pittsburgh Sleep Quality Index (PSQI), and the quality of life of patients was determined according to the Dermatology Life Quality Index (DLQI). Taking into account indicators on the use of a smartphone with the iOS or Android operating system included calculating average screen time, assessing addiction and the fact of using a smartphone 1 hour before bedtime. The statistical processing was carried out using the Jamovi version 2.3.21 program. Results. The SLD were identified in 85 (65.38%) patients with MMA. The dermatosis had a strong impact on quality of life (DLQI20) in 82.31% (n=107). In group I of patients "poor sleeping" there was a direct correlation between SLD (PSQI) and the severity of dermatosis (ρ=0.533; p0.001) and the duration of screen time (ρ=0.628; p0.001) spent on a smartphone per day. The patients of group I had a greater dependence on a smartphone compared to group II of patients "well sleeping": 15.94±1.89 points versus 7.71±1.96 points (p0.001). Conclusion. It has been established that the level of SLD has a high incidence in the group of patients with MMA. There is a pronounced correlation between dyssomnia and the severity of dermatosis; in patients with disturbed sleep, a high dependence on the phone and a correlation with the duration of screen time spent on a smartphone per day were revealed. This study demonstrates the need to pay attention to the presence of disturbances in the quality of sleep in people suffering from skin diseases, in particular acne.

Hydro-Climatic Factors and Health Problems in the Town of Ouéllé (Central-Eastern Côte d'Ivoire)

This article deals with hydro-climatic factors and their health impacts in the town of Ouéllé in a context of climate change. The aim of this study is to show the impact of hydro-climatic determinants on the spatial distribution of pathologies in the town of Ouéllé. The methodological approach used was based on data collection techniques ranging from documentary research to field surveys. Interviews were conducted with experts, while questionnaires were administered to 100 people living in the urban area of Ouéllé. In addition, photographs of the spatial units were taken using a Redmi 9A smartphone, followed by the collection of geospatial data using the UTM Geo map mobile application for data collected in KML (Keyhole Markup Language) format on the Android operating system. We also used statistical data analysis techniques to process the data. The results showed that the main hydro-climatic factors in Ouéllé are rainfall, temperature and wind. The diseases linked to these hydro-climatic factors are malaria, which is the main health risk, accounting for 63% of consultations in 2023, followed by typhoid fever (8%) and acute respiratory infections (ARI) (3%).

Data Synthesis Is Going Mobile—On Community-Driven Dataset Generation for Android Devices

Personal electronic devices such as smartphones and smartwatches have become indispensable daily companions, collecting a multitude of personal and sensitive data. As a result, they are of paramount importance in digital forensic examinations. However, there is a lack of publicly available and ready-to-use digital forensic datasets, especially in mobile forensics. This work presents a concept and an open-source proof-of-concept implementation, which simplifies and automates the creation of mobile forensic datasets within the scope of the Android operating system. In contrast to previous approaches, which populate the most common databases of an Android device, our concept is based on community-driven playbooks and makes use of interaction with the actual smartphone GUI. Hence, we are able to generate coherent and realistic traces as they occur in real-world human usage. Our proof-of-concept implementation is based on the standard Android emulation environment and borrows tools from the user interface testing community. Our evaluation shows that our approach actually generates realistic Android datasets. For instance, we can generate traces that cannot be simulated by gestures (e.g., changing the GPS position or triggering incoming phone calls). Recording the actual data synthesis process allows users to either create and share their own playbooks (i.e., the exact instructions for the data synthesis process rather than having to share the full image) or reproduce Android images with different scenarios using playbooks previously created and shared by the community.

PHIGrader: Evaluating the effectiveness of Manifest file components in Android malware detection using Multi Criteria Decision Making techniques

The popularity of the Android operating system has itself become a reason for privacy concerns. To deal with such malware threats, researchers have proposed various detection approaches using static and dynamic features. Static analysis approaches are the most convenient for practical detection. However, several patterns of feature usage were found to be similar in the normal and malware datasets. Such high similarity in both datasets’ feature patterns motivates us to rank and select only the distinguishing set of features. Hence, in this study, we present a novel Android malware detection system, termed as PHIGrader for ranking and evaluating the efficiency of the three most commonly used static features, namely permissions, intents, and hardware components, when used for Android malware detection. To meet our goals, we individually rank the three feature types using frequency-based Multi-Criteria Decision Making (MCDM) techniques, namely TOPSIS and EDAS. Then, the system applies a novel detection algorithm to the rankings involving machine learning and deep learning classifiers to present the best set of features and feature type with higher detection accuracy as an output. The experimental results highlight that our proposed approach can effectively detect Android malware with 99.10% detection accuracy, achieved with the top 46 intents when ranked using TOPSIS, which is better than permissions, hardware components, or even the case where other popular MCDM techniques are used. Furthermore, our experiments demonstrate that the proposed system with frequency-based MCDM rankings is better than other statistical tests such as mutual information, Pearson correlation coefficient, and t-test. In addition, our proposed model outperforms various popularly used feature ranking methods such as Chi-square, Principal Component Analysis (PCA), Entropy-based Category Coverage Difference (ECCD), and other state-of-the-art Android malware detection techniques in terms of detection accuracy.

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

Mobile App Prototype Supporting People With Special Needs

The article analyses current mobile applications designed for people with special needs. They are aimed at improving routine daily activities and services non-disabled users would not experience significant difficulties. The purpose of the article is to develop and promote a mobile application for people with impaired vision or hearing. In this paper, a mobile application prototype has been designed and developed that would also be useful for anyone wishing to learn sign language. It is compatible with both Android and iOS operating systems. The methodology involves conducting preliminary research and interviews with potential clients. A survey on current mobile applications in the targeted area has been carried out. Functionalities offered by the prototype ensure that the needs and requirements of 'different' users are largely met. As for non-disabled users, the app has the potential to enhance their communication with those specific user groups.

Personal Finance Management Application

The paper presents a personal finance management mobile application developed for the Android operating system. The application is in the process of trials and test deployment among selected customers. It provides opportunities for managing a personal budget, retrieval of a financial status report for a certain period, working with expenses and income, report generation and visualization through charts, and barcode scanning. The methodology for designing and implementing the developed prototype includes pre-testing and preliminary interviews with potential customers who need convenient and easy access to manage their personal finances. Extensive research has been done on the availability of similar desktop and mobile applications. Appropriate technologies have been selected for the implementation of the program logic, software part and user interface. As a result, a mobile application has been developed that provides full integration with the web-based personal finance management system, previously created by the authors. The app is open to future improvements.

Usability, Reliability, Security, and Status: Unpacking the Factors Shaping User Choice between iOS and Android in Saudi Arabia

This investigation delves into user perceptions of iOS and Android operating systems within the Kingdom of Saudi Arabia. A sample of 594 participants was recruited to explore their preferences concerning usability, reliability, security, social influence, and prior experiences with both systems. The study reveals a modest predilection for iOS in terms of user-friendliness and perceived information security. Interestingly, a counterintuitive finding emerged: Android users, particularly females, exhibited high confidence in information security despite encountering a greater number of cyberattacks. Additionally, the research highlights the influence of sociodemographic factors, such as age, education level, and gender, on system selection. A pronounced desire to utilize iOS was observed among younger users, females, and individuals with higher educational attainment. While the study provides a robust foundation for understanding user preferences within the Saudi Arabian context, limitations such as sample size and geographic focus necessitate further research efforts across more heterogeneous populations to achieve a more generalizable depiction.

Risk Score Computation for Android Mobile Applications Using the Twin k-NN Approach

The Android operating system has a dominant market for use within a wide range of devices. Along with the widespread growth of the use of the Android system and the development of a huge number of apps for this operating system, new malicious apps are released daily by adversaries, which are difficult to identify and deal with. This is due to them using sophisticated techniques and strikes. Although there are a diverse range of classification models and risk estimation metrics for identifying malware in this operating system, there is still a requirement for more effective approaches in this context. In this paper, we present a new algorithm to calculate the security risk score of Android apps, which can be used to identify malicious apps from benign ones. This algorithm uses a novel technique named twin k-nearest neighbor. In this technique, to estimate the security risk of an unknown app, its nearest neighbors to malicious apps and its nearest neighbors to normal apps are computed separately using an appropriate distance formula. Then, the security risk of the input app can be computed using a simple formulation. In this formulation, the average distances of both k-nearest malicious apps and k-nearest non-malicious apps to the input app are used. In this way, the proposed method can calculate a high security risk for malware and a lower security risk for goodware. Experimental evaluations on real datasets show that the proposed algorithm has better performance over the previously proposed ones in terms of detection rate, precision, recall, and f1-score.

COVID-19 Nursing Staff Sizing Technology.

This study shows the development of a software for calculating the number of nursing team members required for providing care during the coronavirus disease 2019 pandemic. Study about the development of a technology based on the literature about data and indicators. The indicators were systematized in the following dimensions: institutional, professional, and occupational structure, all with a focus on coronavirus disease 2019. The software was created to be used on the Web, client-server, in browsers such as Internet Chrome, Explorer, and/or Mozilla Firefox, accessing via an Internet address and also allowing access by Windows, Android, and Linux operating systems, with MySQL database used for data storage. The data and indicators related to the institutional structure for coronavirus disease 2019 were systematized with 10 dimensions and indicators, and the professional and occupational structure, with 14 dimensions and indicators. The construction of computer requirements followed the precepts of software engineering, with theoretical support from the area. In the evaluation of the software, data simulation revealed points that had to be adjusted to ensure security, data confidentiality, and easy handling. The software provides to calculate the size and quality of the team, nursing sizing required due to the needs generated by the coronavirus disease 2019 pandemic.

Development of a Novel Mobile Health App to Empower Young People With Type 1 Diabetes to Exercise Safely: Co-Design Approach.

Blood glucose management around exercise is challenging for youth with type 1 diabetes (T1D). Previous research has indicated interventions including decision-support aids to better support youth to effectively contextualize blood glucose results and take appropriate action to optimize glucose levels during and after exercise. Mobile health (mHealth) apps help deliver health behavior interventions to youth with T1D, given the use of technology for glucose monitoring, insulin dosing, and carbohydrate counting. We aimed to develop a novel prototype mHealth app to support exercise management among youth with T1D, detail the application of a co-design process and design thinking principles to inform app design and development, and identify app content and functionality that youth with T1D need to meet their physical activity goals. A co-design approach with a user-centered design thinking framework was used to develop a prototype mHealth app "acT1ve" during the 18-month design process (March 2018 to September 2019). To better understand and respond to the challenges among youth with diabetes when physically active, 10 focus groups were conducted with youth aged 13-25 years with T1D and parents of youth with T1D. Thereafter, we conducted participatory design workshops with youth to identify key app features that would support individual needs when physically active. These features were incorporated into a wireframe, which was critically reviewed by participants. A beta version of "acT1ve" was built in iOS and android operating systems, which underwent critical review by end users, clinicians, researchers, experts in exercise and T1D, and app designers. Sixty youth with T1D, 14 parents, 6 researchers, and 10 clinicians were engaged in the development of "acT1ve." acT1ve included key features identified by youth, which would support their individual needs when physically active. It provided advice on carbohydrates and insulin during exercise, information on hypoglycemia treatment, pre- and postexercise advice, and an educational food guide regarding exercise management. "acT1ve" contained an exercise advisor algorithm comprising 240 pathways developed by experts in diabetes and exercise research. Based on participant input during exercise, acT1ve provided personalized insulin and carbohydrate advice for exercise lasting up to 60 minutes. It also contains other features including an activity log, which displays a complete record of the end users' activities and associated exercise advice provided by the app's algorithm for later reference, and regular reminder notifications for end users to check or monitor their glucose levels. The co-design approach and the practical application of the user-centered design thinking framework were successfully applied in developing "acT1ve." The design thinking processes allowed youth with T1D to identify app features that would support them to be physically active, and particularly enabled the delivery of individualized advice. Furthermore, app development has been described in detail to help guide others embarking on a similar project. Australian New Zealand Clinical Trials Registry ACTRN12619001414101; https://tinyurl.com/mu9jvn2d.

Strategi Manajemen Memori pada Sistem Operasi Android untuk Smartphone Kelas Menengah

In the context of rapidly evolving mobile technology, memory management has become an important aspect that determines the efficiency and effectiveness of Android operating systems, particularly on mid- range smartphones. The objective of this research is to identify, analyze and develop memory management strategies that can improve the performance of the Android operating system on resource-constrained devices. Using an experimental approach, this study evaluates various memory management techniques, including the use of optimized caches, memory compression, and page replacement algorithms, to determine the most effective combination to improve system responsiveness and performance. The results show that the implementation of appropriate memory management strategies can significantly improve operating system performance and user experience while extending battery life. These findings provide valuable information for application developers and device manufacturers to optimize their products for mid-range smartphone competition.

Разработка кроссплатформенного приложения для управления робототехническим комплексом

A brief analysis of the approaches currently used to develop management programs is provided, their application features, advantages and disadvantages are identified. For the study, the development of a cross-platform appli-cation of control systems for robotic complexes (RTC) was chosen, using the example of RTC with MP-11 robotic manipulators. The structure of the control system includes a controller on the Arduino Mega 2560 Pro Mini board with a loaded low-level program and an upper-level program. The development is carried out on the platform.NET MAUI, which allows you to create an application for Windows, Android, and IOS operating systems on a single code base, i.e. the same code is automatically compiled for all operating systems, taking into account their features and applied elements. The control computer is connected via USB, mobile devices via Bluetooth, while data is programmatically transmitted via a serial port. The description of the modes of operation of the developed application, its functioning algorithms, indicating the features of execution for this type of programs and the appearance of forms with elements located on them is given. The program provides manual and automatic modes of operation, as well as control of not only the RTC, but also its model. The key features of this application are the use of a single code base and design for the implementation of desktop and mobile applications for various operating systems without using network resources, unlike web applications.

A Digital Platform for Home-Based Exercise Prescription for Older People with Sarcopenia.

Digital therapeutics refers to smartphone applications, software, and wearable devices that provide digital solutions to improve healthcare delivery. We developed a digital platform to support the GYM (Grow Your Muscle) study, an ongoing 48-week randomized, controlled trial on reduction of sarcopenia through a home-based, app-monitored physical exercise intervention. The GYM platform consists of a smartphone application including the exercise program and video tutorials of body-weight exercises, a wearable device to monitor heart rate during training, and a website for downloading training data to remotely monitor the exercise. The aim of this paper is to describe the platform in detail and to discuss the technical issues emerging during the study and those related to usability of the smartphone application through a retrospective survey. The main technical issue concerned the API level 33 upgrade, which did not enable participants using the Android operating systems to use the wearable device. The survey revealed some problems with viewing the video tutorials and with internet or smartphone connection. On the other hand, the smartphone application was reported to be easy to use and helpful to guide home exercising. Despite the issues encountered during the study, this digital-supported physical exercise intervention could provide useful to improve muscle measures of sarcopenia.

An empirical study on compatibility issues in Android API field evolution

Context:The continuous evolution of the Android operating system requires regular API updates, which may affect the functionality of Android apps. This is becoming increasingly common due to the frequent evolution of the Android platform, which introduces new APIs and deprecates existing ones. Recent studies investigated API evolution to ensure the reliability of Android apps; however, they focused on API methods alone. Objectives:This study aims to understand how API fields evolve and how this affects API compatibility in real-world Android apps and their development. Method:We perform an empirical study on compatibility issues in Android API field evolution by analyzing the nature and resolution of these issues across 681 open-source Android apps. Results:Our experimental results yield interesting findings: (1) On average two API field compatibility issues exist per app in each tag; (2) Although API method evolution and API field evolution are related, current API method-level analysis techniques may fail to detect numerous API field compatibility issues; (3) Different types of checks are preferred when addressing different types of compatibility issues; (4) It takes on average three and a half months for an API field compatibility issue to get fixed since when it is introduced; (5) Developers pay proper attention to API field compatibility issues and address them soon after becoming aware of them in the apps. Conclusion:These findings highlight the significance of including API fields in future research on API evolution and can assist developers and researchers in understanding, detecting, and handling compatibility issues in API field evolution.

Forensic analysis of OpenAI's ChatGPT mobile application

Since its public launch, OpenAI's ChatGPT has achieved significant success, attracting millions of users within the first few months of its release. Although numerous similar applications have emerged, none have yet matched the success of OpenAI's ChatGPT. Last year, OpenAI released the ChatGPT mobile app. This application serves a broad range of uses, some of which may be malicious and, unfortunately, it has not yet been parsed by either commercial or open-source tools. Nevertheless, the data stored by this application, such as JSON files that store a user's conversations with ChatGPT, can be instrumental in attributing user actions, discerning perpetrators' knowledge and motivations, and resolving practical investigations. In this paper, OpenAI's ChatGPT mobile application is examined on both Android and iOS operating systems, focusing on potential evidentiary data within. The cloud-native data associated with the app, which can be retrieved through user data export requests are also investigated. The primary objective of this study is to discover artifacts that investigators can use in real-world cases involving this mobile app. Additionally, the authors have contributed to FOSS to support professionals in this field.

Efficient Malware Detection in Android Devices to Improve Cyber Security

The project introduces a novel framework for detecting Android malware based on permissions, utilizing multiple linear regression methods. Permissions play a crucial role in the security of the Android operating system, serving as fundamental indicators of an application's behavior. Through static analysis, the framework extracts application permissions and employs machine learning techniques to conduct security analyses. Specifically, the framework employs multiple linear regression techniques to develop two classifiers for permission-based Android malware detection. These classifiers leverage the relationships between various permission attributes to accurately identify potentially malicious applications. Notably, the framework achieves notable performance levels using classification algorithms without the need for overly complex models. In the project, the existing system utilizes the Random Forest (RF) algorithm, while the proposed system adopts the Support Vector Machine (SVM) algorithm. Both algorithms are evaluated in terms of accuracy, with the results demonstrating that the proposed SVM approach outperforms the existing RF method. This highlights the effectiveness of SVM in accurately detecting Android malware based on permission analysis.

Sistem Operasi Integrasi Teknologi Augmented Reality dan Virtual Reality dalam Aplikasi Moblo dan Relax untuk Android

This research examines the Android operating system in the context of Augmented Reality (AR) and Virtual Reality (VR) applications with a focus on the Moblo (AR) and Relax (VR) applications. Android, as the primary operating system in mobile devices, offers a powerful and flexible platform for AR and VR application development. Moblo is an AR application that allows users to design and visualize furniture in a real space, while Relax is a VR application that provides meditation and relaxation experiences in a virtual environment. This research aims to understand how the Android operating system supports the development and operationalization of AR and VR applications, as well as evaluating the performance and user experience of these two applications. The results show that Android provides significant support for AR and VR application development, with Moblo and Relax showing good performance and providing a satisfactory user experience. However, further optimization is needed to ensure consistent performance across Android devices.

Comparative analysis of database types in mobile applications running on the Android operating system

The article focused on conducting a detailed comparative analysis of the different types of databases used in mobile applications on the Android platform. The research included four main types of databases: relational (SQLite), key-value (SnappyDB), object-oriented (ObjectBox) and non-relational (Couchbase Lite). The analysis focused on four key operations: writing, reading, editing and deleting data. In addition, the work evaluated each database type in terms of resource consumption, such as RAM and CPU. This research aimed to provide a comprehensive perspective on the performance and efficiency of each database type in the context of mobile applications on the Android platform. The research confirmed that the ObjectBox database has better performance than the other analyzed databases.