Android OS Research Articles

Think About It: Promoting Physical Activity With a Mobile App With a Theory-Based Approach

Currently, people’s highly busy lifestyles and sedentary behavior contribute negatively to multiple health factors. During the COVID-19 pandemic, the different sanitary measures, such as limited mobility and the closing of gyms and sports centers, have contributed to limited physical activity. In this context, there are several apps to enhance physical activity across all mobile stores with an emphasis on mobile sensing. However, the use of a formal theory incorporated into the app development and interventions is less evident. A theory-based approach contributes to understanding the reasons and situations in which an intervention strategy can have an impact. The present work considers the Elaboration Likelihood Model (ELM), which addresses persuasion and attitude change. Can we develop a persuasive app that promotes physical activity based on contemporary attitudes and behavioral change theories? We developed a mobile application for Android OS. Then, 63 participants tested it, and were encouraged to think of ideas or arguments in favor of doing physical activity in a high elaboration task. A mediation analysis was done, with results showing that attitudes partially mediate the association between thought and physical activity. Participants’ thoughts were seen to be positively correlated with their attitudes; and, in turn, participants’ attitudes were correlated with their behavioral intention (to do physical activity). This suggests that a theory-based approach for the active production of biased beliefs is effective when designing an app that encourages positive attitudes toward physical activity.

Android malicious attacks detection models using machine learning techniques based on permissions

The Android operating system is the most used mobile operating system in the world, and it is one of the most popular operating systems for different kinds of devices from smartwatches, IoT, and TVs to mobiles and cockpits in cars. Security is the main challenge to any operating system. Android malware attacks and vulnerabilities are known as emerging risks for mobile devices. The development of Android malware has been observed to be at an accelerated speed. Most Android security breaches permitted by permission misuse are amongst the most critical and prevalent issues threatening Android OS security. This research performs several studies on malware and non-malware applications to provide a recently updated dataset. The goal of proposed models is to find a combination of noise-cleaning algorithms, features selection techniques, and classification algorithms that are noise-tolerant and can achieve high accuracy results in detecting new Android malware. The results from the empirical experiments show that the proposed models are able to detect Android malware with an accuracy that reaches 87%, despite the noise in the dataset. We also find that the best classification results are achieved using the RF algorithm. This work can be extended in many ways by applying higher noise ratios and running more classifiers and optimizers.

Correlation of the Karolinska sleepiness scale with performance variables of the monotonous bimanual psychomotor test

To assess the objectivity of measuring the level of sleepiness in the subjects using a monotonous psychomotor bimanual tapping test developed by us, performed on mobile devices running Android OS. Four hundred and ninety-four hour-long experiments with the performance of a psychomotor test were conducted on 102 students. Using the method of mixed linear models, correlations between the levels of sleepiness according to the Karolinska Sleepiness Scale (KSS) and the Epworth Sleepiness Scale (ESS) and the behavioral indicators of the test were evaluated. Statistically significant correlations between the increase in KSS scores and such indicators as a decrease in the total number of button taps and an increase in the frequency of «microsleep» episodes are shown. Statistically significant correlations of ESS score characteristics with the behavioral indicators of the test were not found. A large statistical material shows a reliable correlation of the parameters of the psychomotor test with the level of sleepiness on the Karolinska scale, which allows using the mobile application developed by us to determine the current level of sleepiness /alertness in the field.

Monotonous sound stimuli embedded with either binaural or monaural beats have the same effect on sleep latency: study on a large subject group

The aim of this study was to test on a large group of subjects the hypothesis that sleep latency (SL) does not depend on the nature of low-frequency beats embedded into monotonous sound stimulus supplied through the fall-asleep process. Specifically, it does not depend on whether those beats are monaural (MB) or binaural (BB). A special application for Android OS was developed and installed on the individual smartphones of 221 subjects for the purpose of the study. Three attempts were performed with each of them using 3 different kinds of monotonous sound supplied according to counterbalanced design. Three kinds of sound were identical in pitch but differed in the beat presence and type: BB, MB or sham (sound without beats). Repeated measures analysis of variance (rANOVA) revealed no significant statistical effect of stimulus type on SL (p=0.21). A pairwise comparison of SL for different stimulation conditions showed the null hypothesis significance level adjusted according to multiple comparison correction to be p=1.0. Thus, in this experiment SL did not significantly depend on the monotonous sound stimulus type: MB, BB, or sham. The software application developed is useful as universal platform to assess at home conditions the impact of various external factors on fall-asleep process.

Can machine learning be used to detect malware?

Nowadays everyone has one or even more than one smartphone or tablet. The existing applications with the most diverse purposes allow us to perform a series of tasks such as using home banking or checking the email, using only our smartphone/tablet. Android OS being one of the most used in this type of equipment becomes an appealing target for viruses, malware and others. At a time when technology is evolving faster and faster, both in terms of hardware and software, Artificial Intelligence has more and more weight in technological evolution, being used in the most diverse purposes. This review aims to demonstrate how Machine Learning can assist in identifying vulnerabilities in Android OS.

Novel Approach for Resolving Android OS Privacy Issues

Android Operating System is an open source which makes developer to design and develop applications easily. Android uses permission-based framework for application which asks users to allow specific permissions in order to install applications. These permissions utilize information associated to different entities like users, systems, devices, and other externalities. The privacy of user’s data is highly associated with allowed permissions as data is transmitted and utilized via these permissions including hardware and software permissions. As android is an open-source platform where hackers can easily intrude into system’s software via allowed permissions and user’s data remains at risk all the time android application is installed on the device. This study intends to design a framework to resolve privacy issues of user’s data in android operating system and to make risk assessment analysis of app-permission model. In order to carry deep analysis of already developed app-permission models, literature is explored and all features related to previously adopted models are analyzed. A novel framework is designed by ranking all required permissions in android operating system and associated risk assessment. The prioritization of all permissions along with associated risk is achieved in this proposed framework. This study will contribute by providing a novel framework to resolve privacy issues of user’s data in android operating system. This proposed framework will ensure user’s data privacy by enforcing allow-assess-revoke mechanism in android applications.

A forensic analysis of streaming platforms on Android OS

This work builds on existing research in streamed video reconstruction on the Android OS, which previously demonstrated that caching occurs in most cases for the Chrome and Firefox Web browsers. Prior work also outlined that streaming application caching behaviour is dependent on both the implementation of the service, as well as the actions taken by the user, with contrasting results between replaying videos and viewing live content. We conduct a forensic investigation for the Twitch, Facebook, Reddit, Instagram and Periscope Android applications, with a focus on the application specific folders in the/data/data directory. Applications were populated with data by creating accounts and viewing a mixture of live and replay (recorded) video streams, with a focus on attempting to recover video fragments or identifiers for particular streams/videos. As users may take action to hinder forensic endeavours, additional videos were viewed to identify baseline caching and overwriting behaviour on each application. Additionally, Android's ‘Cache clear’ operation was evaluated for its anti-forensic potential.While Android seems to produce different behaviour for live and recorded streams, which is consistent with prior work, our findings suggest that Android applications typically retain few, or no, video artefacts, which contrasts with their browser based counterparts. Cache clearing also appears to be a powerful, and trivial, anti-forensics step for clearing locally cached media in each application. We suggest that, going forward, new applications should be tested on a variety of platforms, as it appears that they do not necessarily leave behind consistent forensic traces across versions.

A Critical Analysis on Android Vulnerabilities, Malware, Anti-malware and Anti-malware Bypassing

<p>Android has become the dominant operating system for portable devices, making it a valuable asset that needs protection. Though Android is very popular; it has several vulnerabilities which attackers use for malicious intents. In this paper, we present a comprehensive study on the threats in Android OS that various malware developers exploit and the different malware functionality based on Android’s threats. Furthermore, we analyze and evaluate the anti-malware approaches implemented to face the malware functionalities. Finally, we analyze and categorize malware developers’ most common anti-analysis techniques to evade anti-malware approaches. It comes to our attention that many papers covered each topic separately; however, we could not find one comprehensive study that covers Android with such details that it could be used as a research handbook on Android malware. This is the main novelty and contribution of this work.</p> <p> </p>

Scientific Work Independence Application at Various Education Levels Referring to Piaget's Theory to Support the Implementation of Science Integrated Learning Model

The research specifically aims to create an application of scientific work independence instruments through Science Integrated Learning (SIL) for various education levels (primary, secondary, tertiary). The SIL model is applied in learning to find specific indicators for scientific work independence following students' development. This study applied the Research and Development methodology. Product testing was conducted in elementary schools, junior high schools, senior high schools, and universities. Based on the research results, SIL V1.0 App is a software developed based on mobile apps in the form of an Android application that is installable on a device with Android OS as the operating system. The application is named SIL V1.0 Apps, an acronym for Science Integrated Learning Application. The scientific work independence instrument developed by referring to Piaget's theory has a characteristic where the level of independence at each stage of scientific work is based on the development of children's learning psychology. Scientific work independence application at various education levels, which refers to Piaget's Theory, has more precise boundaries because it is adjusted to children's psychological development. The difference in learning independence level occurs because of the age difference.

MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric

Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example of a symmetric model of communication, and app collusion, where apps collaborate dynamically with unknown malware apps, is an example of a serious threat with an asymmetric pattern. The symmetric nature of app collaboration can become vulnerable when a vulnerability called PendingIntent is exchanged during Inter-Component Communication (ICC). The PendingIntent (PI) vulnerability enables a flexible software model, where the PendingIntent creator app can temporarily share its own permissions and identity with the PendingIntent receiving app. The PendingIntent vulnerability does not require approval from the device user or Android OS to share the permissions and identity with other apps. This is called a PI leak, which can lead to malware attacks such as privilege escalation and component hijacking attacks. This vulnerability in the symmetric behavior of an application without validating an app’s privileges dynamically leads to the asymmetric phenomena that can damage the robustness of an entire system. In this paper, we propose MULBER, a lightweight machine learning method for the detection of Android malware communications that enables a cybersecurity system to analyze multiple patterns and learn from them to help prevent similar attacks and respond to changing behavior. MULBER can help cybersecurity teams to be more proactive in preventing dynamic PI-based communication threats and responding to active attacks in real time. MULBER performs a static binary analysis on the APK file and gathers approximately 10,755 features, reducing it to 42 key features by grouping the permissions under the above-mentioned four categories. Finally, MULBER learns from these multivariate features using evolutionary feature selection and the Mahalanobis distance metric and classifies them as either benign or malware apps. In an evaluation of 22,638 malware samples from recent Android APK malware databases such as Drebin and CICMalDroid-2020, MULBER outperformed others by clustering applications based on the Mahalanobis distance metric and detected 95.69% of malware with few false alarms and the explanations provided for each detection revealed the relevant properties of the detected malware.

Secure Device-to-Device Caching With Blockchain

Caching and distributing of network packets among devices has been accepted by the research community as a cost-effective method of transferring especially audio-video data among mobile users. Another newly established technology, blockchain, has been successfully applied to decentralized financial systems like Bitcoin. The research on caching and distributing methods has not focused on the security aspect so far. In this article, we present an effective scheme to provide security and integrity of network packets in caching and distribution systems by employing the blockchain technology. Unlike the existing blockchain applications, the proposed protocol requires a lightweight consensus algorithm. Our scheme also allows peers in the network to make correctly addressed inquiries as the packets’ locations are kept in the blocks which are available to any peer in the network. Considering that cryptographic primitives alone are not adequate to prevent malicious network packets among the peers, the proposed blockchain application presents an invaluable tool to prevent malicious packets from leaking into the system and to address which peers own the desired packets. We demonstrate the efficiency of the proposed method by implementing it for Android OS devices and conduct real-time testing on different settings.

Literature Review of Mobile Security Enhancement

Abstract: The presented article provides an overview of related literature on the subject of mobile security. The issue was selected as a result of the increase in mobile applications and the underdeveloped discussion surrounding the security of those applications. Cell phones and tablets with mobile operating systems are regarded as mobile devices for the purposes of this essay. More specifically, these are BlackBerry OS (RIM)., iOS, and Android from Google, respectively Even though it's crucial to understand these concepts, the security flaws in the Android OS are the main subject of this literature review. Malware that changes to be somewhat different from the previous version is referred to as polymorphic. Although the malware's functioning is unaffected by the automated code changes, they may make traditional anti-virus detection technologies ineffectual.

Aplikasi Ruangan Maya Berbasis Android OS pada Headset Virtual Reality Oculus Quest 2

Metaverse and VR (virtual reality) technology brings a lot of benefits to some sectors of life, especially education because VR technology makes it possible for users to be placed in a simulation of a certain scenario or situation which helps with the user’s learning experience. The development of computer technology where computer components are much smaller in size, have better computational capacity, and low cost has made VR technology more available and offers more and more opportunities as computer technology develops even further. In order to take advantage of the increasing opportunity, a virtual room application based on Android OS for Oculus Quest 2 is developed in order to learn about virtual reality development workflow on a VR headset using Unity game engine. The features implemented in the virtual room app are locomotion control by teleportation and continuous movement, rotation by snap turning, interactable objects such as a mirror, grabbable objects which the user can hold in the virtual room, and sockets to place digital objects in the virtual room.

A Comparative Study of Machine Learning Techniques for Android Malware Detection

The rapid growth and wide availability of Android applications in recent years has resulted in a spike in the number of sophisticated harmful applications targeting Android users. Because of the popularity and amount of open-sourced supported features of Android OS, cyber attackers prefer to target Android-based devices over other smartphones. Malicious programs endanger user privacy and device integrity. To address this issue, the authors investigated machine learning algorithms for detecting malware in Android in this study. They employed a static analysis approach, collecting permissions from each application's APK and then generating feature vectors based on the extracted permissions. Finally, they trained several machine learning algorithms to create classification models that can distinguish between benign and malicious applications. According to experimental findings, random forest and multi-layer perceptron approaches, which have accuracy levels of 95.4% and 95.1%, respectively, have the best classification performance.

Achieving resource-centric access control for web-app interactions on android

The capability of interacting with web content has become increasingly common among mobile apps. While web-app interaction can facilitate many new functionalities and improve app user experience, they also cause various notable security attacks on mobile apps or web content. The root cause is lack of proper access control mechanisms for web-app interactions on mobile OSes. Existing solutions usually adopt either an origin-centric design or a code-centric deign, and suffer from one or several of the following limitations: coarse protection granularity, poor flexibility in terms of access control policy establishment, and incompatibility with existing apps/OSes due to the need of modifying the apps and/or the underlying OS. More importantly, none of the existing works can organically deal with all the five web-app interaction mechanisms. In this paper, we first identify and survey five mechanisms through which web content interacts with mobile apps. We then propose ReACt , a novel Re source-centric A ccess C on t rol design that can coherently work with all the web-app interaction mechanisms while addressing the above-mentioned limitations. We have implemented a prototype system on Android, and performed extensive evaluation on it. The evaluation results show that our system works well with existing commercial off-the-shelf Android apps and different versions of Android OS, and it can achieve the design goals with small overhead.

An Empirical Study on Architecture and Efficiency of Dalvik VM and Android Runtime (ART)

Abstract: Many mobile platforms have developed as a result of the development of mobile technology. Rapid technological advancement has led to the widespread adoption of mobile devices with excellent graphics. Since its inception, the Open Handset Alliance project known as Android has undergone numerous revisions. Being centred on efficiency and pace, it's development. Android OS is driven by the Dalvik microkernel. The Android OS is sluggish due to the runtime as it is a virtual system. Android's multitasking capability leads to its latency. Earlier, there have been efforts to speed up Dalvik, but they were inefficacious. Increasing Android's speed and optimization was severely hampered by this. The Android Runtime (ART), which takes the role of the Dalvik Virtual Machine, made its debut when Android 4.4 was released by Google. A fast gadget was made possible by ART, which skillfully blends portability and performance, which eventually enhanced graphics optimization. A pre-compiled program was maintained for the Android applications by ART, which was not feasible with Dalvik Virtual machine. A speed of double has been observed on the Android Operating system, in several experiments. This paper discloses the architecture of android virtual machines (Dalvik and ART), memory optimization and efficiency of both in various devices. Also lists out the for and against of the latter one over the former and the basic functions of the runtimes

Leveraging the first line of defense: a study on the evolution and usage of android security permissions for enhanced android malware detection

Android security permissions are built-in security features that constrain what an app can do and access on the system, that is, its privileges. Permissions have been widely used for Android malware detection, mostly in combination with other relevant app attributes. The available set of permissions is dynamic, refined in every new Android OS version release. The refinement process adds new permissions and deprecates others. These changes directly impact the type and prevalence of permissions requested by malware and legitimate applications over time. Furthermore, malware trends and benign apps’ inherent evolution influence their requested permissions. Therefore, the usage of these features in machine learning-based malware detection systems is prone to concept drift issues. Despite that, no previous study related to permissions has taken into account concept drift. In this study, we demonstrate that when concept drift is addressed, permissions can generate long-lasting and effective malware detection systems. Furthermore, the discriminatory capabilities of distinct set of features are tested. We found that the initial set of permissions, defined in Android 1.0 (API level 1), are sufficient to build an effective detection model, providing an average 0.93 F1 score in data that spans seven years. In addition, we explored and characterized permissions evolution using local and global interpretation methods. In this regard, the varying importance of individual permissions for malware and benign software recognition tasks over time are analyzed.

Malware detection for Android application using Aquila optimizer and Hybrid LSTM-SVM classifier

INTRODUCTION: Android OS is the most recent used smartphone platform in the world that occupies about 80% in share market. In google play store, there are 3.48 million apps available for downloading. Unfortunately, the growth rate of malicious apps in google play store and third party app store has become a big concern, which holds back the development of the Android smartphone ecosystem. OBJECTIVES: In recent survey, a new malicious app has been introduced for every 10 seconds. These malicious apps are built to accomplish a variety of threats, such as Trojans, worms, exploits, and viruses. To overcome this issue, a new efficient and effective approach of malware detection for android application using Aquila optimizer and Hybrid LSTM-SVM classifier is designed. METHODS: In this paper, the optimal features are selected from the CSV file based on the prediction accuracy by cross validation using Aquila optimizer and the mean square error (MSE) obtained by the cross validation is consider as the fitness function for the Aquila to select the optimal features. RESULTS: The extracted optimal features are given to the Hybrid LSTM-SVM classifier for training and testing the features to predict the malware type in the android system. CONCLUSION: This proposed model is implemented on python 3.8 for performance metrics such as accuracy, precision, execution time, error, etc. The acquired accuracy for the proposed model is 97%, which is greater compared to the existing techniques such as LSTM, SVM, RF and NB. Thus, the proposed model instantly predicts the malware from the android application.

Comparing the Efficiency of Malware Detection in Android System

Abstract: Personal Digital Assistants are becoming fundamental in our lives. Android is perhaps the most well-known working framework. Android OS is far-reaching in the portable business today due to its easy and comprehensive engineering. Application clients will generally trust the Android Operating System to get information; however, it is being demonstrated that Android is more defenseless and eccentric. of concern. As a result of its open-source design, the Android OS is endless in the versatile business today. It is a wide assortment of uses and essential highlights. Application clients generally prefer Android OS to get information

Android-Based Mobile Devices communication Using Peer 2 Peer Network Channels

The interest grew between scholars and researchers for exploring some of the facts about how we can channelize the usage of P2P to a wide variety of modern day applications. The usages namely for P2P applications includes Instant Messaging, file Sharing, Voice over IP, Video streaming and social media. This paper tells about the methodologies on how P2P works in general for getting a complete Abstraction for below application protocols and even a summarized description on how P2Pised applications work. Moreover, this paper tells about peering and how sharing is done in P2P application for easing work of developers. Furthermore, the paper provides the limitations and different obstacles in adoption of P2P in the cellphone’s technological environment. Lastly, there is a conclusion on a bit of presentation of direction for future research so that appropriate middleware can be developed and decided for appropriate use-cases for implementation in Android OS.