Mobile Operating System Research Articles

Dark-Side Avoidance of Mobile Applications With Data Biases Elimination in Socio-Cyber World

The accessibility of mobile apps takes into account the rights and interests of various social groups, which is vital for the millions of smartphone users who are visually impaired given the variety of mobile applications available on Google Play and the App Store. Most application icons, however, lack natural language labels. It is challenging for these users to engage with mobile phones utilizing screen readers featured in mobile operating systems. Millions of visually impaired smartphone Internet users’ inability to communicate with mobile applications have become the socio-cyber world’s dark side. COALA is a pilot work that solves this issue by generating the textual label from the imaging icon automatically. However, most icon datasets have imbalance distributions in the real-world scenario that only a few categories have rich-resource labeled samples, and the major rest categories have very limited samples. To address the data imbalance problem in the icon label generation task, we provide an interconnected two-stream language model with mean teacher learning, which learns a generalized feature representation from divergent data distributions. Extensive experiments demonstrate the superiority of our two-stream language model over previous single-language models on different low-resource datasets. More experimental results reveal that our method outperforms the COALA model by a wide margin in decreasing the dark side of the socio-cyber world.

Transformative Implementation of Android-Based Point of Sale System at Shafira’s Buffet Stall

Shafira’s Buffet Stall is a small eatery located at 37 Gubeng Kertajaya V Raya Street. They offer a diverse range of delicacies, from fried snacks to vegetable dishes. The typical business process at the stall involves customers either dining in or requesting takeaways. After making their selections, the customers proceed to the cashier to have their orders tallied. Observing this process, the idea of developing an Android-based Point of Sale (POS) system struck the author. This system aims to streamline and record all transactions efficiently. The Point-of-Sale system is designed to facilitate seamless transactions and includes a cashier module to record sales. The decision to develop this POS application was driven by the need for accurate transaction records and seamless management. The Android platform was chosen due to its popularity as the most widely used mobile operating system in Indonesia, boasting an impressive 89% market share as of December 28, 2022. Additionally, in urban areas of East Java, around 68.75% of the population utilize mobile phones, further justifying the choice of the Android platform for the system. The application offers features such as login credentials for two types of accounts: cashier and administrator (faktur). It allows access to functions like managing transactions, adding new menu items, listing available menu items, and generating daily and weekly transaction reports. The implementation and testing of the application were performed through two iterations. After incorporating the necessary improvements based on black box testing, all features functioned smoothly. Usability testing resulted in a remarkable 5.73% increase in overall user satisfaction. This Android-based POS application proves to be an invaluable tool for the business owner, providing insights into popular menu items and their daily and weekly sales. It assists in making informed decisions regarding menu restocking and daily sales strategies.

DroidExaminer: An Android Malware Hybrid Detection System Based on Ensemble Learning

<p>Android is an open-source mobile operating system, with more than 70% of the mobile market share, widely popular on various intelligent devices. At the same time, the number of new malicious applications keeps increasing every year. In this paper, we first discuss the advantages and disadvantages of various detection methods for malicious software. A single detection method can only cover specific types of malware. Therefore, we propose a system that combines static structural analysis and dynamic detection of malware. This system has dual detection capability, which consists of a client and a server. The client is a lightweight Android application that is used to obtain the relevant data information of the installation package. The server is responsible for static analysis of APK and dynamic running of monitoring logs to get the relevant feature information. Based on the feature information, the Bagging algorithm of ensemble learning is adopted, and the decision tree and random forest are combined to identify the malware accurately. We collected 4210 Android software samples, with malicious apps accounting for about 20% of the total. Cross-testing of malware detection on this sample set showed that DroidExaminer achieved approximately 96% accuracy in detecting malware. It can resist confusion and conversion techniques, and the test performance overhead is less. In addition, DroidExaminer can alert the user to the details of malware intrusion so that the user can prevent malware intrusion.</p> <p> </p>

DETECTION OF MALICIOUS ACTIVITY USING A NEURAL NETWORK FOR CONTINUOUS OPERATION

This article describes the problem of detecting malicious programs in running systems of users of mobile applications. Because users can download any application on their phone, which over time can pull up additional settings, which can store malicious routines for monitoring both personal life and their personal data, such as logins, passwords, bank data. The detection of such routines is based on dynamic analysis and is formulated as a weakly controlled problem. The article contains an analysis of information on the development of researchers who worked on detection models and methods such as: statistical and dynamic intrusion detection methods, anomaly detection model, settings classification methods, machine and deep learning methods. Machine learning, and especially deep learning, has become an extremely useful and interesting topic in cybersecurity over the past few years. In this context, the detection of malicious software has received considerable attention. The article considers the problem of detecting the activity of malicious software of mobile operating systems in the time domain by analyzing behavioral sequences of a large amount of industrial data. When malware executes on a system, its behavior consists of a series of distinct actions placed along the time axis, and there is only a subsequence of actions that lead to malicious activity. Very often, malicious software does not manifest itself immediately, and at some point in the execution, malicious activity is formed. Therefore, the main task and difficulty is to identify such a subsequence in the entire sequence of events. Due to this, it is proposed to develop a behavior model that would analyze the dynamic behavior of the program in the system during execution. For this, a sequence of API/function calls generated by the program at runtime is used as input data and a recurrent neural network (RNN) architecture is proposed to detect malicious activity. The article describes the training method of the proposed model and provides verification of its performance on a large sample of industrial data consisting of a large number of samples generated on the emulator farm. Many mobile phone vendors strive for hardware acceleration on the device to provide better support. Therefore, it can be considered that the deployment of a model based on RNM directly on the device as one of the security levels can become a viable solution. The test data of the model described in the article show sufficiently high positive results when detecting malicious activities.

F3l: an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android

Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security. Machine learning and deep learning have become mainstream methods to analyze traffic, and labeled dataset construction is the basis. Android occupies a huge share of the mobile operating system market. Instant Messaging (IM) applications are important tools for people communication. But such applications have complex functions which frequently switched, so it is difficult to obtain function-level labels. The existing function-level public datasets in Android are rare and noisy, leading to research stagnation. Most labeled samples are collected with WLAN devices, which cannot exclude the operating system background traffic. At the same time, other datasets need to obtain root permission or use scripts to simulate user behavior. These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained. Previous work (Chen et al. in Appl Sci 12(22):11731, 2022) proposed a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C, running at the application-level in Android. This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android, F3L. The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground. It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions. The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels. Experiments show that the method can match the correct label within 3 s after the user operation.

On the Complexity of the Web’s PKI: Evaluating Certificate Validation of Mobile Browsers

Digital certificates are frequently used to secure communications between users and web servers. Critical to the Web's PKI is the secure validation of digital certificates. Nonetheless, certificate validation itself is complex and error-prone. Moreover, it is also undermined by particular constraints of mobile browsers. However, these issues have long been overlooked. In this paper, we undertook the first systematic and large-scale study of the certificate validation mechanism within popular mobile browsers to highlight the necessity of reassessing it among all released browsers. To this end, we first compile a comprehensive test suite to identify security flaws in certificate validation from various aspects. By designing and implementing a generic, automated testing pipeline, we effectively evaluate 30 popular browsers on two mobile OS versions and compare them with five representative desktop browsers. We found the latest mobile browsers Accept as many as 33.2% invalid certificates and Reject merely 5.4% invalid ones on average, leaving the majority of them to be decided by users who usually have little expertise. Our findings shed light on the severity and inconsistency of certificate validation flaws across mobile browsers, which are likely to expose users to MITM attacks, spoofing attacks, and so forth.

Mobile Operating Systems’ Impact on Customer Value: IOS vs. Android

ABSTRACT Amidst the growing focus on media engagement and customer value in retail marketing literature, mobile commerce (MC) research has gained prominence. This research explores how customers employ mobile operating systems to engage with retailers and extract value within the context of Fast Moving Consumer Goods (FMCG) in retail. In Study 1, a survey involving 398 users uncovered that the customer handset OS moderates the effects of social media, traditional media engagement, and retail “place” on customer value. In Study 2, leveraging data from a foreign FMCG brand deeply immersed in social media platforms, we scrutinize how such engagement dynamics affect the influence of “place” on product sales across e-commerce and conventional retail channels. Our findings make significant theoretical contributions to comprehending customer value in MC, with practical implications for marketers, emphasizing the potential of customer mobile operating system as a valuable tool for effective marketing strategies.

The Real Time Monitoring System and Early Detection of Unbalanced Load for the Three Phase Distribution Systems in Urban Areas

This paper introduces a three-phase unbalanced load condition monitoring system in a power distribution network that can monitor and provide early warning. The distribution system is a component of the power system that must always be observed in maintaining the service of delivering electricity to customers. Currently, many technologies are applied to the distribution system that can ensure the operation of the distribution network, especially distribution transformers to determine real-time transformer conditions so that damage and power losses can be minimized, but do not provide early detection. Therefore, the role of information technology is currently one of the investments that must be made for all electric power supply operators in maintaining the performance of distribution transformers through early detection. In this study, the system developed based on Internet of Things (IoT) technology that was built consists of: embedded system devices that function to carry out measurements and transmit data, Web Service software that functions to receive data from sensor systems, and Arduino-based software using the Blynk Cloud application platform in presenting data and managing data through Android display devices (HP/Laptop). The Blynk is a compatible platform for applications on Mobile OS (iOS and Android) in controlling Arduino, Raspberry Pi, or ESP8266-based modules, as well as similar modules via the Internet (IoT). This system was built to provide an early warning function against network parameter anomalies such as overload, overvoltage, and unbalanced load. After getting the data, the system will perform calculations to obtain recommendations for system optimization in the form of balancing, maintenance, or load maneuver. Based on testing, the time needed to send and receive notifications on smartphone users via the blynk application ranges from 3-4 seconds. By setting a tolerance limit of 20%, when the load is severely unbalanced where the neutral current is almost equal to or greater than the phase current, the local indicator light will light up and the Android system will provide an unbalanced load indicator.

Potentially Unwanted App Detection for Blockchain-Based Android App Marketplace

Android is a mobile operating system with a high degree of openness, which attracts an increasing number of developers. Android application (or simply, app) marketplace provides a trusted source of apps for users and a more equitable competition environment for individual developers and commercial teams. Blockchain’s advantages of decentralization and data immutability are suitable for the Android app marketplace, which is mainly characterized by openness, equality, and security. However, this may also facilitate malicious developers to publish low-quality apps to display ads or steal users’ privacy for revenue. Therefore, blockchain-based app marketplaces have a strong need to identify those potentially unwanted apps (PUAs). In this paper, we first introduce our blockchain-based app marketplace model. Then, we propose a new PUA detection method, mainly based on metadata and user ratings, and they are easily accessible from blockchain-based app marketplaces. Moreover, we introduce dynamic analysis to check whether the URLs visited by the app are in malicious URL blacklists since apps with massive access to these URLs tend to affect user experience. After that, we pre-process those complex and redundant features and represent each app as an embedding. Finally, to validate the effectiveness of our method, we utilize several clustering algorithms to represent these apps as clusters and search for suspicious PUA clusters. Our study reveals several characteristics of PUA and suggests that PUAs are still present and need to be urgently removed.

Android Security: Malware Detection with Convolutional Neural Network and Feature Analysis

Android is a mobile operating system based on a modified version of the Linux kernel and other open-source tools. Due to its system efficiency and the multitude of features it offers to users, the Android operating system has taken a leading position in the technology market and often attracts the attention of cybercriminals. As malware continues to evolve, traditional methods for detecting Android malware, such as signature-based approaches, may not be sufficient to detect the latest malware threats. Therefore, this research proposes a deep learning algorithm, specifically Convolutional Neural Network (CNN) and Component Analysis (PCA), for feature extraction to enhance the accuracy of Android malware detection. The dataset used in this study is the CICAndMal2017 dataset. Testing results are evaluated using three parameters: accuracy, precision, and recall. Experimental results indicate that our deep learning approach outperforms many other methods with an accuracy of 91%.

Android Malware Detection using Permission Based Static Analysis

The increase of mobile device enhancement grows. With this development, mobile phones are supporting many programs, and everyone takes advantage of them. Nevertheless, malware applications are increasing more and more so that people can come across lots of problems. Android is a mobile operating system that is the most used on smart mobile phones. Because it is the most used and open source, it has been the target of attackers. Android security is related to the permissions allowed by users to the applications. There have been many studies on permission-based Android malware detection. In this study, a permission-based Android malware system is analyzed. Unlike other studies, we propose a permission weight approach. Each of the permissions is given a different score using this approach. Then, K-nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms are applied, and the proposed method is compared with the previous studies and the expected experimental results of the proposed approach will be higher.

ANDROID-BASED EDUCATIONAL APP FOR LEARNING THE PRAYER PROCESS

Prayer is one part of prayer. There are several obligatory prayers for Muslims: morning, noon, Asar, Maghrib, and Isha. By following the development of mobile technology, prayer learning applications can be developed to be used anywhere and at any time without an internet connection. Android is a Linux-based mobile operating system designed by Android. Various activities can be done using a smartphone, such as working, playing, entertaining, and studying. Using multimedia development, it is hoped that users will be interested in learning how to pray. The application developed includes several pages containing material evidence, pillars, preparation, and prayer guides.

Android-Based Bus Vehicle Maintenance Application Design at PT. Sumber Jaya Trans Tangerang

Sumber Jaya Trans is a tourism bus transportation company whose goal is to make tourism easier. Maintenance of bus vehicles is required because of their long-term use. At PT. Sumber Jaya Trans Tangerang has problems, namely vehicle maintenance in terms of data reporting which is still manual, maintenance reports are carried out on a paper form filled in by mechanics who have repaired bus vehicles and recapitulated into the ledger, managers have difficulty viewing or checking maintenance reports, because lots of paper forms and disorganized ledgers. Due to the issues at hand, the author suggests an Android-based bus vehicle maintenance app. utilizing the PIECES technique of analysis. The programming languages for Web Services and Android are PHP and the Laravel framework, respectively, and object-oriented analysis techniques are used in the system architecture together with the Unified Modeling Language (UML). Android was selected as the mobile operating system because it is well-liked in Indonesia and felt capable of replacing manual labor. In order to improve the effectiveness and efficiency of reporting vehicle maintenance data, we require an Android-based solution

What is a File on a Phone? Personal Information Management Practices Amongst WhatsApp Users

The file, as a means of sharing structured information across time and place, has spanned centuries of ICTs. Despite much CSCW research on file management practices in organizational desktop computing settings, little research has examined how files are managed and shared using mobile devices. Our research asks, how do mobile phones and mobile applications impact the personal information management (PIM) practices involving files for young adults? In this interview and task observation study, we examined the file management practices of mobile phone users (N=25), both Android and iOS owners who use the superapp WhatsApp. Superapps like WeChat and WhatsApp are increasingly characterized as technology of life for the majority world, particularly among mobile-first users. We find that many grammars of action related to managing digital files in personal computing and email contexts persist, but participants' sharing practices are influenced by media ideologies of friends, family, colleagues; and that their file management practices are shaped by the affordances of WhatsApp, as well as mobile operating systems and device hardware constraints. These results will be useful to researchers examining collective PIM practices; mobile communication practices between users and businesses via messaging apps; and theories of personal information practices related to the sharing and storage of digital files among mobile-first cohorts who increasingly share and access information via platforms and mobile applications.

Comparative Analysis of Display Color Accuracy Across Different Mobile Operating System

Comparative Analysis of Display Color Accuracy Across Different Mobile Operating System

Relevant markets and market power of mobile apps

Antitrust investigations in the mobile app economy often require a definition of the relevant market of mobile apps and the evaluation of their market power. However, existing antitrust tools face significant challenges due to the non-price nature of mobile apps with multiple revenue sources, two competition margins at the mobile OS and mobile app levels, and the switching costs involved in the choice of mobile OSs. In this paper, we provide a description of the mobile app economy, including its essential components, players, and characteristics, and identify the challenges currently observed in antitrust investigations. We propose a model for mobile app users and advertisers that can address these issues and suggest a method for defining the relevant market of mobile apps and evaluating their market power, which is adaptable to markets with zero-priced products or multiple revenue sources beyond mobile apps.

MobiPCR: Efficient, accurate, and strict ML-based mobile malware detection

Mobile devices have been and will be continuously prevalent as rich applications are provided for various demands. However, the mobile operating system lacks efficient malware detection tools, which puts personal data at risk. This paper presents MobiPCR, a trict, accurate, efficient mobile-oriented malware detection system. MobiPCR basically integrates a (n) (edge) cloud-based architecture, a powerful yet efficient machine learning-based detection model, and a neat detection process. We implemented the MobiPCR prototype system and conducted rigorous experiments to evaluate its performance from different perspectives. We implemented the MobiPCR prototype system on the Android platform (Installer Hooker part) consiering considering that Android is an open-source platform that (i) can be easily modified and (ii) provides rich documentation. We used LineageOS 13 (a widespread Android distribution) to provide the necessary drivers to support communication and the camera for casual usage. Experimental results prove that MobiPCR can strictly and accurately detect malwares and outperform existing similar applications without extra operations.

Design and Implementation of Campus Maintenance and System Repair with Integration of Mobile Technology for E-commerce

The Android mobile application terminal operating system is constantly updated and developed, as well as the continuous optimization, promotion and application of Android smart phones and continuous updating of software technology, coupled with the continuous promotion of digital information engineering construction in various industries, the widespread use of smart phones, Android has gradually been hailed as a mobile application terminal and operating system with the largest number of users in the world. This system studies the design and implementation of the campus maintenance and system repair based on mobile technology. The purpose is to help the schools manage better and it allows students to participate more actively in the construction of the campus, and at the same time it provides teachers and students with a relaxed and happy lifestyle on campus. This article describes UI design and operations that can be used to maintain and repair Android phones on campus, including implementing the main interface, using controls, program operation, and resolving abnormal situations. The development of this system can bring more enthusiasm and good experience to users.

Permission-Based Classification of Android Malware Applications Using Random Forest

Android is arguably the most widely used mobile operating system in the world. Due to its widespread use, it has attracted a lot of attention of cybercriminals who attempt to exploit its architecture and outsmart innocent users to install malware applications. The number of such applications is growing every day either by alternating a basic exploitation mechanism or by creating novel mechanisms to exfiltrate users’ data. As a result, there is an increasing need for detection mechanisms that can classify these applications to families based on their characteristics. A significant amount of research has already been devoted to analysing and mitigating this growing problem; however, this situation demands more efficient methods with higher precision. The paper proposes such a framework for analysing and classifying a malicious application to certain families relying on the permissions used. The proposed method involves the pre-processing of the applications to extract their permissions, the tokenization of permissions, the data cleansing and finally the application of the Random Forest Classifier to classify the applications in families. The proposed method is trained and tested with a dataset of 11,159 malicious applications categorized in 33 unique families. The precision, recall and f1-score achieved is 98%. The results of the proposed methodology are promising, since it even works in an unbalanced dataset and in many cases outperform other state-of-the-art approaches.

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.