Android Environment Research Articles

Subject Review:Encryptionof Image in the Android Environment in Various Algorithms

Subject Review:Encryptionof Image in the Android Environment in Various Algorithms

Reproducibility and equivalence of GDLAM protocol mobile application for the evaluation of functional autonomy

This study aimed to develop a mobile application (App) for the Latin American Group for the Development of the Elderly (GDLAM) protocol of functional autonomy in the Android environment and check its reproducibility. This study was divided into two stages: 1) creation and development of the GDLAM App, Android Studio software, and JAVA as a programming language; 2) evaluation of 63 volunteers for the GDLAM protocol using the traditional method and the App. The significance level was set at p<0.05. There were no statistical differences between the traditional GDLAM protocol and the App. However, the total time required to assess the GDLAM protocol using the App was significantly lower (p <0.001). In addition, the levels of agreement between assessments exhibited strong and very strong positive correlations with significance (p <0.001) for all variables. The study showed that the use of the GDLAM App is as efficient as the traditional functional autonomy protocol for the elderly, demonstrating that reproducibility is adequate, assessment time shorter, and results are obtained faster.

Call Graph and Model Checking for Fine-Grained Android Malicious Behaviour Detection

The increasing diffusion of mobile devices, widely used for critical tasks such as the transmission of sensitive and private information, corresponds to an increasing need for methods to detect malicious actions that can undermine our data. As demonstrated in the literature, the signature-based approach provided by antimalware is not able to defend users from new threats. In this paper, we propose an approach based on the adoption of model checking to detect malicious families in the Android environment. We consider two different automata representing Android applications, based respectively on Control Flow Graphs and Call Graphs. The adopted graph data structure allows to detect potentially malicious behaviour and also localize the code where the malicious action happens. We experiment the effectiveness of the proposed method evaluating more than 3000 real-world Android samples (with 2552 malware belonging to 21 malicious family), by reaching an accuracy ranging from 0.97 to 1 in malicious family detection.

Hybrid classification of Android malware based on fuzzy clustering and the gradient boosting machine

The widespread use of smartphones in recent years has led to a significant rise in the sophistication and number of Android malicious applications (apps) targeting smartphone users. Android-based smartphones attract attackers more than other smartphones due to the popularity and open source features of the Android environment. Malware apps pose a potential threat to the security of Android-based smartphones because of the storage of confidential and private information. To improve the classification efficiency of Android malicious apps, this paper proposes a hybrid approach for the classification of Android malware by integrating the fuzzy C-means clustering (FCM) algorithm with the light gradient boosting machine (LightGBM). First, the fuzzy clustering method is utilized for generating highly significant clusters of the Android apps’ permissions that reflect the certain characteristics of the Android apps. The primary goal of using fuzzy clustering is to produce new features by gathering Android app permissions with related patterns together in clusters. Second, the LightGBM is employed to take the app’s permissions and their clusters resulting from FCM as inputs and outputs the classification result as a malware or goodware app after the training. The advantages of using LightGBM are its high learning efficiency and precise classification. The significance of the proposed approach is illustrated by conducting several experiments using a well-known dataset containing benign and malware Android apps. The results of the experiments show that the suggested approach outperforms the other approaches and attains the highest accuracy (94.63%), area under the curve (AUC) (98.74%) and precision (97.70%).

Performance Enhancement Method for Machine Learning Algorithm

Machine learning is programming computer or a mobile device that learns from experience. Machine learning classification methods are helpful in various fields of Computer Science like driverless cars, product recommendation systems, dynamic pricing, Google translate, online video streaming, internet and mobile fraud detection systems and much more. The present work proposes a method augClassifier to enhance the performance of Simple Logistics machine learning algorithm. The performance assessment of machine learning algorithm is conducted on a Mobile device using Android Environment. The work also presents the comparative performance investigations of Simple Logistics machine learning algorithm using correlation based feature selection method with respect to performance measures Precision, Sensitivity, F-Measure and ROC. The present work conforms that the augClassifier enhances the performance of Simple Logistics machine learning algorithm.

Data Collection Practices of Mobile Applications Played by Preschool-Aged Children

Child-directed mobile applications (apps) have been found to collect digital identifiers and transmit them to third-party companies, a potential violation of federal privacy rules. This study seeks to examine the differences in app data collection and sharing practices by evaluating the sociodemographic characteristics of the children who play them. To examine data collection and sharing practices of 451 apps played by young children and to test associations with child sociodemographic characteristics. This study used data from the baseline phase of the Preschooler Tablet Study, a prospective cohort study conducted from August 2018 to January 2020. This study used a population-based sample. A convenience sample of the parents of preschool-aged children was recruited from pediatric offices, childcare centers, social media posts, and an online participant registry. Eligibility criteria included (1) parent or guardian of a child aged 3 to 5 years, (2) parent or guardian who lived with the child at least 5 days per week, (3) participants who spoke English, and (4) a child who used an Android (Google LLC) device. All interactions with participants were through email, online surveys, and mobile device sampling. Sociodemographic characteristics were assessed by parental report. This study tested the hypothesis that data transmissions to third-party domains are more common in apps played by children from low-socioeconomic-status homes. Child app usage was assessed via a mobile sampling app for an average of 9 days. Persistent identifier data transmissions to third-party domains were quantified for each app using an instrumented Android environment with monitoring of network traffic; for each child, the counts of total data transmissions were calculated, and the total third-party domains were detected for the apps they played. Our sample comprised 124 children who used Android devices (35 tablets, 89 smartphones; 65 girls [52%]; mean [SD] age, 3.85 [0.57] years; 87 non-Hispanic White [71%]). One hundred twenty of participating parents (97%) were women. Of 451 apps tested, 303 (67%) transmitted persistent identifiers to 1 to 33 third-party domains. Child data transmission counts ranged from 0 to 614 (median [interquartile range], 5.0 [1-17.5]) and third-party domain counts from 0 to 399 (4.0 [1-12.5]). In multivariable negative binomial regression models, higher transmission and third-party domain rates per app were positively associated with older age (rate ratio, 1.67 [95% CI, 1.20-2.33]; P = .002 and 1.69 [95% CI, 1.26-2.27]; P < .001, respectively) and lower parent educational attainment (eg, high school or General Educational Development or less rate ratio, 2.29 [95% CI, 1.20-4.39]; P = .003 and 2.05 [95% CI, 1.13-3.70]; P < .02, respectively), but not with household income. This study found that apps used by young children had a high frequency of persistent identifier transmissions to third-party companies, suggesting that federal privacy rules are not being enforced. Older children, those with their own devices, or those from lower-education households may be at higher risk of potential privacy violations.

Ask a(n)droid to tell you the odds: probabilistic security-by-contract for mobile devices

Security-by-contract is a paradigm proposed for the secure installation, usage, and monitoring of apps into mobile devices, with the aim of establishing, controlling, and, if necessary, enforcing security-critical behaviors. In this paper, we extend this paradigm with new functionalities allowing for a quantitative estimation of such behaviors, in order to reveal in real time the more and more challenging subtleties of new-generation malware and repackaged apps. The novel paradigm is based on formal means and techniques ranging from statistical analysis to probabilistic model checking. The framework, deployed in the Android environment, is evaluated by examining both its effectiveness with respect to a benchmark of real-world malware and its effect on the execution of genuine, secure apps.

A Large Scale Analysis of Android — Web Hybridization

Many Android applications embed webpages via WebView components and execute JavaScript code within Android. Hybrid applications leverage dedicated APIs to load a resource and render it in a WebView. Furthermore, Android objects can be shared with the JavaScript world. However, bridging the interfaces of the Android and JavaScript world might also incur severe security threats: Potentially untrusted webpages and their JavaScript might interfere with the Android environment and its access to native features.No general analysis is currently available to assess the implications of such hybrid apps bridging the two worlds. To understand the semantics and effects of hybrid apps, we perform a large-scale study on the usage of the hybridization APIs in the wild. We analyze and categorize the parameters to hybridization APIs for 7,500 randomly selected and the 196 most popular applications from the Google Playstore as well as 1000 malware samples. Our results advance the general understanding of hybrid applications, as well as implications for potential program analyses, and the current security situation: We discovered thousands of flows of sensitive data from Android to JavaScript, the vast majority of which could flow to potentially untrustworthy code. Our analysis identified numerous web pages embedding vulnerabilities, which we exemplarily exploited. Additionally, we discovered a multitude of applications in which potentially untrusted JavaScript code may interfere with (trusted) Android objects, both in benign and malign applications.

Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation

Android currently represents the most widespread operating system focused on mobile devices. It is not surprising that the majority of malware is created to perpetrate attacks targeting mobile devices equipped with this operating systems. In the mobile malware landscape, there exists a plethora of malware families exhibiting different malicious behaviors. One of the recent threat in this landscape is represented by the HummingBad malware, able to perpetrate multiple attacks for obtain root credentials and to silently install applications on the infected device. From these considerations, in this paper we discuss two different methodologies aimed to detect malicious samples targeting Android environment. In detail the first approach is based on machine learning technique, while the second one is a model checking based approach. Moreover, the model checking approach is able to localize the malicious behaviour of the application under analysis code, in terms of package, class and method. We evaluate the effectiveness of both the designed methods on real-world samples belonging to the HummingBad malware family, one of the most recent and aggressive behaviour embed into malicious Android applications.

QR based management system for plants shopping center

&lt;span&gt;It is well known that the development in the life style requires a robust enhancement in the provided services based on technologies. This paper suggests a mobile application under android environment that manages the information and sale systems regarding plants shop. The proposed application uses the Quick Response (QR) codes as identification of an object (plant). The proposed system includes two parts: server and client. The server side contains the most software application, such as database, while the client (mobile) side runs the website based interface between the application and users. QR tag is attached to each plant on the farm to be read by visitor’s mobile camera and sent to the server side using the designed web application. The received QR code is processed by the server and preparing the response with full information of a plant to be sent back to the mobile for user viewing. In addition, the proposed system offers a sale service to the user after performing registration. The proposed system is tested over different case studies and the obtained results show the efficiency of its performance.&lt;/span&gt;

Enhanced Android Malware Detection and Family Classification, using Conversation-level Network Traffic Features

Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised-based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14‬% for precision and recall, respectively

Gait Recognition as an Authentication Method for Mobile Devices.

With the rate at which smartphones are currently evolving, more and more of human life will be contained in these devices. At a time when data privacy is extremely important, it is crucial to protect one’s mobile device. In this paper, we propose a new non-intrusive gait recognition based mechanism that can enhance the security of smartphones by rapidly identifying users with a high degree of confidence and securing sensitive data in case of an attack, with a focus on a potential architecture for such an algorithm for the Android environment. The motion sensors on an Android device are used to create a statistical model of a user’s gait, which is later used for identification. Through experimental testing, we prove the capability of our proposed solution by correctly classifying individuals with an accuracy upwards of 90% when tested on data recorded during multiple activities. The experiments, conducted on a low sampling rate and at short time intervals, show the benefits of our solution and highlight the feasibility of an efficient gait recognition mechanism on modern smartphones.

Development of a mobile application to screen and manage fall risks in older people

Objective Falls and related injuries are known to be the major health problem leading to disability and mortality among older adults. Identification and management of fall risks aimed to prevent falling is considered to be essential. The aim of this study was to develop a mobile application for screening and management of fall risks based for older adults. Method and materials First, we determined product features based on literature reviews. Then we asked 10 experts to assess the importance of capabilities. The application was designed in Android environment based on the STEADI toolkit. Finally, the usability and satisfaction of the application were assessed among 30 older adults by means of a usability questionnaire, and the final version was modified. Results The experts rated the majority of feasibility features to be important. The application consisted of falls risk self-assessment, and the Timed Up and Go (TUG) test to measure individual fall risks, fall prevention education, and suggestions for training based on the individuals' level of fall risk among older adults. The results of the usability assessment showed that the users were satisfied with the application, 8.83 out of 10. Conclusion The mobile application can be used to screen and manage fall risks in older adults. It may help health providers to identify older adults at low, moderate, and high risks of falls provide education and training to minimise falls and fall-related injuries. Implications for rehabilitation Falls are a major health problem in older people. A great proportion of falls and falls related injuries are preventable. Given the availability and accessibility of various mobile health application and the increasing mobile device usage among older adults, mobile application can be used as a platform for delivering fall prevention programmes such as education and exercise training. The mobile application may be a valuable tool in the fall prevention and their consequences either in old adult.

Android malware detection based on image-based features and machine learning techniques

In this paper, a malware classification model has been proposed for detecting malware samples in the Android environment. The proposed model is based on converting some files from the source of the Android applications into grayscale images. Some image-based local features and global features, including four different types of local features and three different types of global features, have been extracted from the constructed grayscale image datasets and used for training the proposed model. To the best of our knowledge, this type of features is used for the first time in the Android malware detection domain. Moreover, the bag of visual words algorithm has been used to construct one feature vector from the descriptors of the local feature extracted from each image. The extracted local and global features have been used for training multiple machine learning classifiers including Random forest, k-nearest neighbors, Decision Tree, Bagging, AdaBoost and Gradient Boost. The proposed method obtained a very high classification accuracy reached 98.75% with a typical computational time does not exceed 0.018 s for each sample. The results of the proposed model outperformed the results of all compared state-of-art models in term of both classification accuracy and computational time.

Design and Implementation of Vehicle Collision Detection and Alarm System Based on Smartphone

Every year, there are a large number of people died in traffic accidents in our country. If people can get help in time after a traffic accident, the survival rate of the people who are treated can be greatly increased. Therefore, it is very necessary to have a system that can detect traffic accidents and call the police or notify the rescuers in time. According to the survey and analysis, the functions of some similar systems in China are not perfect and most of the cost are high, which cannot be widely promoted in China with a high private car base. Based on the acceleration sequence value obtained by the smartphone accelerometer, this project designs the vehicle collision detection algorithm and realizes the collision detection during the driving process of the vehicle in the android environment. When a collision event is detected, the system can promptly send the collision information to the preset phone number and drive the camera function of the mobile phone, perform instant photo forensics on the scene, and finally send the help information and the accident photo to the preset phone number. The project system can realize the detection and alarm function of automobile collision accidents in time, and provide timely rescue after the collision accident.

Нова методика оцінки пожежного ризику

Ukraine is at the last places at the international ratings concerning safety of life. It is related first of all to the fields of technogenic safety and fire safety. The values of annual losses due to fires, accidents and due to other emergency situations reach billions of hryvna, which is the essential part of the national budget and they have the increasing trends. Such situation is common for almost all branches of the industry of Ukraine except for the nuclear energy of Ukraine, which is under the additional guidance and under the additional control of the international organizations. The authors of this paper consider the prevalence of obsolete paradigm of safety management based on inspection control without risk evaluations and the insignificant adoption of the digital technologies. Modern safety management should be based on the detailed analysis of the working conditions, personnel safety modelling, popula-tion and the environment, causes of the possible emergency situations, possible errors of the personnel and thus identifying measures and means to prevent fires and accidents and reducing possible conse-quences. It should be the modern information technology based on the paradigm of risk-based approach, in general. This technology implies the existence of databases, accumulation of the statistical data with prompt professional processing of them, modelling of dangerous situations with live risk assessment, with permanent risk monitoring and the measures to maintain the risk at the acceptable level. Methods and algorithms for solving these problems must be defined, which allow automated processing of cur-rent values of risk parameters by computer devices in Windows and Android environments. The authors propose new algorithms for monitoring the current risk situation based on a modernized method of fail-ure and consequence analysis, by detailed analysis of loss due to partial failures of security systems, and summarizing the results of analysis by convolution in all areas of inspection and all hazardous objects.

Incremental Research on Cyber Security metrics in Android applications by implementing the ML algorithms in Malware Classification and Detection

Cyber attacks are prevailing to be a great headache for the technical advancements especially when dealt with mobile usage in an android application environment. For a new user, it is difficult to identify the set of harmful permissions. This could be an advantage for malware intruders to access the data or infect the mobile device by introducing malware applications. Thus the face of Cybersecurity has changed in recent times with the advent of new technologies such as the Cloud, the internet of things, mobile/wireless, and wearable technology. The technological advances in data science which help develop contemporary cybersecurity solutions are storage, computing, and behavior. In this paper, the possible investigations are done on the cyber attacks in android by adopting the various malware classification and detection techniques. Various Classifications and Detections are done on various malware prevailing in the android applications.

TrCMP: A dependable app usage inference design for user behavior analysis through cyber-physical parameters

Abstract Timely and accurate service recommendations can help users improve their quality of life and efficiency of work. The accuracy of service recommendations often depends on effective user behavior analysis. Technically, user behaviors associated with a specific service could be reflected in the apps they use, while operating different apps will change cyber-physical parameters on Android devices, such as power consumption, CPU usage, memory usage, and traffic data. Some studies have already leveraged cyber-physical parameters to analyze app usage. However, these investigations usually take into consideration a single parameter (e.g., traffic or power), which could be easily affected by the complex Android environment. Moreover, to the best of our knowledge, these investigations cannot analyze some apps that run in the offline state. In this paper, we study the similarities and the differences of parameters with various apps running in both online and offline states. Then we propose a design of a dependable app usage inference, named TrCMP to understand the inference in a mobile system through a combination of the cyber and physical system parameters. This design comprehensively considers Tr affic, C PU, M emory and P ower to speculate apps running in both online and offline states. An algorithm is proposed to find the most effective weight values for each parameter. Extensive experiments are conducted to validate the effectiveness and dependability of our design.

Distribution of Mobile Apps Considering Cross-Platform Development Frameworks in Android Environment

Distribution of Mobile Apps Considering Cross-Platform Development Frameworks in Android Environment

Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation

Aiming at stroke patients’ hand rehabilitation training, we present a hand exoskeleton with both active and passive control modes for neural rehabilitation. The exoskeleton control system is designed as a human–robot interaction control system based on field-programmable gate array (FPGA) and Android mobile terminal with good portability and openness. Passive rehabilitation pattern based on proportional derivative (PD) inverse dynamic control method and active rehabilitation pattern based on impedance method, are established respectively. By the comparison of the threshold value and the force on the fingertip of the exoskeleton from the sensor, the automatic switch between active and passive rehabilitation mode is accomplished. The hand model is built in Android environment that can synchronize the movement of the hand. It can also induce patients to participate in rehabilitation training actively. To verify the proposed control approach, we set up and conduct an experiment to do the passive rehabilitation mode, active rehabilitation mode, and active plus passive mode experimental researches. The experiment results effectively verify the feasibility of the exoskeleton system fulfilling the proposed control strategy.