Mobile Operating System Research Articles

An Intelligent Scheduling Approach on Mobile OS for Optimizing UI Smoothness and Power

Mobile devices need to respond quickly to diverse user inputs. The existing approaches often heuristically raise the CPU/GPU frequency according to the empirical rules when facing burst inputs and various changes. Although doing so can be effective sometimes, the existing approaches still need improvements. For instance, raising processors’ frequency can lead to high power consumption when the frequency is over-provisioned or fails to meet user demands when the frequency is under-provisioned. To this end, we propose MobiRL, a reinforcement learning-based scheduler for intelligent adjusting CPU/GPU frequency to satisfy user demands accurately on mobile systems. MobiRL monitors the mobile system status and autonomously learns to optimize UI smoothness and power consumption by conducting CPU/GPU frequency-adjusting actions. The experimental results on the latest delivered smartphones show that MobiRL outperforms the widely used commercial scheduler on real devices – reducing the frame drop rate by 4.1% and reducing power consumption by 42.8%, respectively. Moreover, compared with a study using Q-Learning for CPU frequency scheduling, MobiRL achieves up to 2.5% lower frame drop rate and reduces power consumption by 32.6%, respectively. Our approach has been deployed in mobile phone products.

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques. However, the issues presented by modern malware cannot be met by traditional signature or heuristic-based malware detection methods. Previous research suggests that machine-learning classifiers can be utilised to analyse permissions, making it possible to differentiate between malicious and benign applications on the Android platform. There exist machine-learning methods that utilise permission-based attributes to build models for the detection of malware on Android devices. Nevertheless, the performance of these detection methods is dependent on the raw or feature datasets. Android malware research frequently faces a major obstacle due to the lack of adequate and up-to-date raw malware datasets. In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis. To create the dataset, we collect recent raw malware samples (APK files) and focus on the reverse engineering approach and permission-based features extraction. We also conduct a thorough feature analysis to determine the important Android permissions and present a machine-learning-based Android malware detection mechanism. The experimental result of our study demonstrates that with just 48 features, the random forest classifier-based Android malware detection model obtains the best accuracy of 97.5%.

A Survey on Mobile Digital Forensic: Taxonomy, Tools, and Challenges

ABSTRACTModern time largely rely on mobile gadgets as a means of communication, and entertainment. These gadgets now plays a vital role in forensic analysis and criminal investigations as it store a plethora of private and sensitive data. Investigating feasible digital incidents and preserving mobile device security became crucial as the proliferation of mobile devices continues to influence modern society. In this survey; mobile threats, investigative process models for mobile forensics, evidence sources, forensics tools, and field problems are all covered in‐depth. Further, a comprehensive analysis examining the capabilities, features, and efficiency of available distinct mobile forensic tools namely Belkasoft, MOBILedit, and Magnet AXIOM for retrieving, analyzing, and presenting digital evidence is being presented. The objective of the study is to assist forensic investigators in choosing the best tool for specific investigations. The findings of our study reveals the complexity of mobile operating systems, investigative process supported with sources of evidence, and forensic tools which makes it clear that the area of mobile forensics is dynamic and require modern skill adaptation along and innovative development in future. Finally, the study presents current challenges encountered during mobile forensic investigations as researchers can enhance their procedures and tactics for mobile device examination and evidence extraction by fostering a greater knowledge of the difficulties involved in mobile forensics.

Protecting Judicial Decisions in the Age of Artificial Intelligence A New Challenge to the Rule of Law

In a world where a large percentage of children are learning how to interact expertly with electronic devices and navigate mobile operating systems before they can even speak, and have grown up with the non-existence of the idea of “disconnecting” with the constant flow of content on the screen, it is no longer possible to reject development and adapt to the requirements of the new stage as we are on the brink of the fifth industrial revolution [1] in which artificial intelligence technologies merge with the work of the human element, and robots coexist with humans. Whatever the name given to the next technological era, and whatever the number it will be labeled with, it will be the last era of inventions and discoveries that humans alone will accomplish exclusively. This prompts experts in the field to consider that “merging with artificially intelligent technology will be like learning how to live with a new gender” [2]. In recent years in particular, artificial intelligence technology has achieved remarkable successes that have proven its pioneering capabilities, and the discussion about it has not remained confined to academic circles, but has occupied the front rows in the halls of official decision-making, and competing voices have risen around it from the highest platforms [3]. The contributions of AI technologies to combating the coronavirus pandemic are still fresh in our minds. Robots have been used to reduce human contact; there are nursing robots, delivery robots, surveillance drones, sterilizing robots, and mobile robots that detect infected people on the street. Big data technologies are also being used to examine surveillance cameras on the streets to recognize faces, as happened in China, where an algorithm combines the health record, the criminal file, and the public transport travel map to identify all the people who have been in contact with the infected person, and quarantine him as a result [4]. Because this reality has begun to cast its shadow on the judicial systems with the entry of artificial intelligence systems into the justice sector, imposing its challenges on them, and even throwing its problems in their face, the judicial decision has become facing serious challenges that raise legitimate questions on the research table, the answers to which converge with the strategic direction of Oman Vision 2040 in the field of legislation, judiciary, and oversight in terms of strengthening the rule of law in society and achieving community security, and in general, it addresses the application of the foundations of governance in the organization of legislation and judiciary, and revolves around the extent of the benefit of introducing artificial intelligence systems in the stage of preparing the judicial decision and the limits of this benefit on the one hand (section one), and the extent of the possibility of using these systems in the stage of building the judicial decision itself on the other hand (section two).

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.

Evaluating the role of cloud integration in mobile and desktop operating systems

The integration of cloud computing has fundamentally transformed the landscape of mobile and desktop operating systems, enabling new levels of functionality, efficiency, and user experience. This review examines the role of cloud integration in these operating systems and its implications for both users and developers. Cloud integration enhances mobile and desktop operating systems by offering scalable storage solutions, seamless data synchronization, and enhanced computing power. For mobile systems, cloud services provide users with the ability to access their data and applications from any device, promoting a consistent user experience across different platforms. This synchronization feature not only streamlines personal data management but also enables developers to create more dynamic and responsive applications that leverage cloud resources for realtime updates and performance improvements. In desktop environments, cloud integration facilitates the storage of large volumes of data and the execution of resourceintensive applications without burdening local hardware. Cloudbased virtual machines and application delivery platforms offer users the flexibility to run highperformance software on less powerful devices, thus broadening the accessibility and usability of advanced desktop applications. Furthermore, cloud integration supports collaborative tools and shared workspaces, enhancing productivity and fostering realtime collaboration among users. The evaluation of cloud integration also involves assessing its impact on security, data privacy, and system performance. Cloudbased solutions introduce new security considerations, such as the need for robust encryption and secure authentication protocols to protect sensitive data. Additionally, the reliance on internet connectivity for accessing cloud services can pose challenges for users in areas with limited or unreliable network access. In conclusion, cloud integration plays a crucial role in modernizing both mobile and desktop operating systems, driving improvements in data accessibility, application performance, and user experience. As cloud technologies continue to evolve, their influence on operating systems will likely expand, offering new opportunities for innovation and addressing emerging challenges in security and connectivity. This ongoing integration represents a significant shift towards more flexible, efficient, and interconnected computing environments.

Developing a framework for enhancing security testing of android applications

Mobile applications have advanced a lot and now offer several features that help make our lives easier. Android is currently the most popular mobile operating system, and it is susceptible to exploitation attempts by malicious entities. This has led to an increased focus on the security of Android applications. This dissertation proposed the development of a framework which provides a systematic approach to testing the security of Android applications. This framework was developed based on a comprehensive review of existing security testing methodologies and tools. In achieving the study objectives, a test application was run on an emulator, Burp Suite was used as a proxy tool to capture HTTP and HTTPS traffic for analysis, reverse engineering was carried out, static and dynamic analysis were executed, network traffic was captured and analysed with tcpdump and Wireshark, intent sniffing was carried out, fuzz testing was discussed, and a proof-of-concept tool (automation script) was developed. This work covers various aspects of Android applications’ security testing, and the proposed framework provides developers with a practical and effective approach to testing the security of their Android applications, thereby improving the overall security of the Android application ecosystem.

Peran Sistem Operasi Seluler dalam Meningkatkan Efisiensi Energi dan Keamanan Mobile Banking untuk Perekonomian Berkelanjutan

In the digital era, mobile operating systems (OS) play a pivotal role beyond mere application execution platforms. They are essential for optimizing energy efficiency and enhancing security, particularly in the realm of mobile banking. This paper explores the critical role of mobile OS in supporting sustainable economic growth through efficient energy use and robust security measures in mobile banking transactions. The focus is on how these OS advancements contribute to a secure and environmentally responsible digital economy. Key aspects covered include energy optimization strategies, security enhancements such as data encryption and multi-factor authentication, and the integration of these technologies into mobile banking applications. By addressing these challenges, mobile OS not only ensures sustainable economic development but also fosters financial inclusivity and security in an increasingly digital world.

FILO: Automated FIx-LOcus Identification for Android Framework Compatibility Issues

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps.

RetrieveDroid: k-NN Performance Evaluation of Distance Measurement Schemes for Android Malware Detection Using Case-Based Retrieval

One of the big challenges in cybersecurity is the detection of Android attacks since Android is the most popular mobile operating system. Within this system, applications require certain permissions to access critical resources. Investigation of the use of permissions is a concern to check whether an application is not mislead to divulge sensitive information. This work aims to determine whose distance schemes offers the best malware detection based on permission similarities. Case based reasoning (CBR) is a concept which aims to find a solution based on historical experiences. CBR performance relies on finding similarities between actual cases and stored cases and then to deduce solutions. This paper proposes to transform app data as vector of appearance of dangerous permissions and to store such vectors based on CBR structure. Then we investigate k-NN classification performance related to five distance-based metrics such as Euclidean, Cosine, Manhattan, Minkowski, and Mahalanobis. Experiments were carried out with a set of 419 applications, including 203 malicious and 216 benign samples. The whole dataset has been split in training set of 291 samples with 162 benign and 129 malicious, and the testing set of 128 samples with 54 benign and 74 malicious samples. k-Nearest Neighbor (k-NN) are used as the similarity algorithm in which the distance model is varied in each of the five distance models. Results reveal that Minkowski and Manhattan models provide the best overall performance to detect Android malware based on permissions, in terms of accuracy (99.21%) and precision (97%). This work is a good start to recommend to researchers distance metrics exploitable when performing permission similarity-based detection.

Next-generation bioinformatics: An ultrafast and user-friendly tool for phylogenomic data exploration.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS and Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

A lightweight deep learning-based android malware detection framework

Android, as the most prevalent mobile operating system (OS) in recent years, has been widely applied in various cell phones, tablets, and embedded devices, greatly facilitating people’s lives. However, Android malware is also emerging, which significantly endangers people’s information security. The current Android malware detection systems are often suffering from cumbersome structures and massive computational resources, which seriously limits their direct deployment on mobile devices. We design a lightweight Android malware detection system named MCADS, which consists of a two-layer structure. At the first layer, we use an augmented Multilayer Perceptron (MLP) for the preliminary analysis of malware. At the second layer, we propose a new lightweight variant of Convolutional Neural Network (CNN) to further analyze the Apps that cannot be accurately identified in the first layer. Finally, a careful experimental investigation has been conducted. The proposed method achieves an accuracy of 98.12%, surpassing that of the compared methods. The promising results demonstrate the potential of MCADS as a practical adjunctive solution for detecting malware, complementing existing complex detection methods.

Approximate vs. precise location in popular location-based services

ABSTRACT Location-based services can provide many benefits to their users but sharing one’s location with third parties also incurs privacy risks. As one way to mitigate this, the two major mobile operating systems provide an approximate location feature, which enables users to only share their approximate location instead of their precise location. In order to assess the usefulness of this feature in practice, we analysed 36 iOS and 36 Android apps regarding the impact of using approximate location had on the functionality and usability in practical use. To achieve this, we used each app following a structured methodology with both precise and approximate location and recorded differences in app behaviour. Our results show that using approximate location caused many of the apps’ functionalities to stop working, although some functionalities were more affected than others. We also observed differences regarding which functionalities were most affected and which operating system the app was running on. Our results shed light on the practical use of approximate location in common apps and can benefit researchers interested in location privacy and app use.

A Multi-model Fusion Strategy for Android Malware Detection Based on Machine Learning Algorithms

In the digital age, the widespread use of Android devices has led to a surge in security threats, especially malware. Android, as the most popular mobile operating system, is a primary target for malicious actors. Conventional antivirus solutions often fall short in identifying new, modified, or zero-day attacks. To address this, researchers have explored various approaches for Android malware detection, including static and dynamic analysis, as well as machine learning (ML) techniques. However, traditional single-model ML approaches have limitations in generalizing across diverse malware behaviors. To overcome this, a multi-model fusion approach is proposed in this paper. The approach integrates multiple machine learning models, including logistic regression, decision tree, and K-nearest neighbors, to improve detection accuracy. Experimental results demonstrate that the fusion method outperforms individual models, offering a more balanced and robust approach to Android malware detection. This methodology showcases the potential of ensemble techniques in enhancing prediction accuracy, providing valuable insights for future research in cybersecurity.

Meta-Learning for Multi-Family Android Malware Classification

With the emergence of smartphones, Android has become a widely used mobile operating system. However, it is vulnerable when encountering various types of attacks. Every day, new malware threatens the security of users’ devices and private data. Many methods have been proposed to classify malicious applications, utilizing static or dynamic analysis for classification. However, previous methods still suffer from unsatisfactory performance due to two challenges. First, they are unable to address the imbalanced data distribution problem, leading to poor performance for malware families with few members. Second, they are unable to address the zero-day malware (zero-day malware refers to malicious applications that exploit unknown vulnerabilities) classification problem. In this paper, we introduce an innovative meta -learning approach for m ulti-family A ndroid m alware c lassification named Meta-MAMC , which uses meta-learning technology to learn meta-knowledge (i.e. the similarities and differences among different malware families) of few-family samples and combines new sampling algorithms to solve the above challenges. Meta-MAMC integrates (i) the meta-knowledge contained within the dataset to guide models in learning to identify unknown malware, and (ii) more accurate and diverse tasks based on novel sampling strategies, as well as directly adapting meta-learning to a new few-sample and zero-sample task to classify families. We have evaluated Meta-MAMC on two popular datasets and a corpus of real-world Android applications. The results demonstrate its efficacy in accurately classifying malicious applications belonging to certain malware families, even achieving 100% classification in some families.

SEGUL: Ultrafast, memory-efficient and mobile-friendly software for manipulating and summarizing phylogenomic datasets.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop an ultrafast and memory-efficient software, SEGUL, that performs common phylogenomic dataset manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line interface (CLI) and graphical user interface (GUI) applications, and as a library for Rust, R and Python, with possible support of other languages. The CLI and library versions run native on Windows, Linux and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS, iPadOS and Android operating systems. SEGUL leverages the high performance of the Rust programming language to offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL's efficiency reduces economic barriers by allowing analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

Effective and Efficient Android Malware Detection and Category Classification Using the Enhanced KronoDroid Dataset

Android is the most widely used mobile operating system and responsible for handling a wide variety of data from simple messages to sensitive banking details. The explosive increase in malware targeting this platform has made it imperative to adopt machine learning approaches for effective malware detection and classification. Since its release in 2008, the Android platform has changed substantially and there has also been a significant increase in the number, complexity, and evolution of malware that target this platform. This rapid evolution quickly renders existing malware datasets out of date and has a degrading impact on machine learning-based detection models. Many studies have been carried out to explore the effectiveness of various machine learning models for Android malware detection. Majority of these studies use datasets that have compiled using static or dynamic analysis of malware but the use of hybrid analysis approaches has not been addressed completely. Likewise, the impact of malware evolution has not been fully investigated. Although some of the models have achieved exceptional results, their performance deteriorated for evolving malware and they were also not effective against antidynamic malware. In this paper, we address both these limitations by creating an enhanced subset of the KronoDroid dataset and using it to develop a supervised machine learning model capable of detecting evolving and antidynamic malware. The original KronoDroid dataset contains malware samples from 2008 to 2020, making it effective for the detection of evolving malware and handling concept drift. Also, the dynamic features are collected by executing the malware on a real device, making it effective for handling antidynamic malware. We create an enhanced subset of this dataset by adding malware category labels with the help of multiple online repositories. Then, we train multiple supervised machine learning models and use the ExtraTree classifier to select the top 50 features. Our results show that the random forest (RF) model has the highest accuracy of 98.03% for malware detection and 87.56% for malware category classification (for 15 malware categories).

STABILITY ANALYSIS OF CELLULAR OPERATING SYSTEM MARKET SHARE IN INDONESIA WITH THE COMPETITIVE LOTKA-VOLTERRA MODEL

The current increase in smartphone users has caused various mobile device operating system companies to compete with each other to create a mobile device operating system that is suitable and acceptable to the public. Competition among mobile operating system market shares can be analyzed using the Lotka-Volterra model. This study aims to reconstruct the Lotka-Volterra model for the cellular operating system market share in Indonesia. In addition, a stability analysis was carried out, which aims to determine the stability of the competitive model for the market share of the operating system in Indonesia. The results of the study show that a competitive Lotka-Volterra model can be built on the Android and iOS operating system market share in Indonesia. In this model, there are four equilibrium points, one of which is unstable, and the other three equilibrium points are conditionally stable.

Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory

The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature.

Development and application of operating systems and shells in mobile technologies: analysis of the history of development and current trends in the field of mobile OS and shells

The objects of research are mobile operating systems and their shells. The subject of the study is the functionality of the Android, iOS and HarmonyOS operating systems, their history of creation and development trends. The authors consider in detail such aspects of the topic as the history of the creation of Android, iOS, HarmonyOS operating systems and TouchWiz, HTC Sense, MIUI and others shells, modern trends in the field of mobile operating systems, which reflect the influence of technological innovations and geopolitical aspects on the development of this sphere. They conduct a detailed analysis of the OS using performance testing applications (AnTuTu Benchmark, 3DMark Benchmark). The purpose of this research is to study the history of the development of mobile OS and shells from the origins to the current trends of technological progress. Research methods are based on the collection and systematization of information, analysis and comparison of systems, as well as performance tests. The scientific novelty of this article lies in the use of OS and shells in mobile devices that meet all the needs and requirements of the user, given the rapid development of digital technologies and their increasing introduction into our daily lives. The main conclusions of the study are the identification of the most common OS, the definition of modern trends, which include the integration of artificial intelligence, multimodality, security and privacy, as well as the expansion of flexibility and portability. The rapid development of technology and the universe of mobile applications makes mobile OS and shells key components of a successful user experience in the world of mobile technology. Understanding the history and current trends in the field of mobile operating systems and shells will allow to more accurately predict technological changes and potential impacts in the future.