scholarly journals Factors that Influence the Android Apps Permissions

2016 ◽  
Vol 1 (2) ◽  
pp. 59
Author(s):  
Normi Sham Awang Abu Bakar ◽  
Iqram Mahmud
Keyword(s):  
Not Given ◽  
Sensitive Data ◽  
Security Issues ◽  
Android Apps ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
The Right ◽  
User Ratings ◽  
Average User

The Android Market is the official (and primary) storefor Android applications. The Market provides users with average user ratings, user reviews, descriptions, screenshots,and permissions to help them select applications. Generally, prior to installation of the apps, users need to agree on the permissions requested by the apps, they are not given any other option. Essentially, users may not aware on some security issues that may arise from the permissions. Some apps request the right to manipulate sensitive data, such as GPS location, photos, calendar, contact, email and files. In this paper, we explain the sources of sensitive data, what the malicious apps can do to the data, and apply the empirical software engineering analysis to find the factors that could potentially influence the permissions in Android apps. In addition, we also highlight top ten most implemented permissions in Android apps and also analyse the permissions for the apps categories in Android.

scholarly journals A first look at Android applications in Google Play related to COVID-19

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Jordan Samhi ◽  
Kevin Allix ◽  
Tegawendé F. Bissyandé ◽  
Jacques Klein
Keyword(s):  
Mobile Apps ◽  
Contact Tracing ◽  
Location Tracking ◽  
Response Effort ◽  
Sensitive Data ◽  
Android Apps ◽  
Digital World ◽  
Public Health Organizations ◽  
Android Applications ◽  
Google Play

AbstractDue to the convenience of access-on-demand to information and business solutions, mobile apps have become an important asset in the digital world. In the context of the COVID-19 pandemic, app developers have joined the response effort in various ways by releasing apps that target different user bases (e.g., all citizens or journalists), offer different services (e.g., location tracking or diagnostic-aid), provide generic or specialized information, etc. While many apps have raised some concerns by spreading misinformation or even malware, the literature does not yet provide a clear landscape of the different apps that were developed. In this study, we focus on the Android ecosystem and investigate Covid-related Android apps. In a best-effort scenario, we attempt to systematically identify all relevant apps and study their characteristics with the objective to provide a first taxonomy of Covid-related apps, broadening the relevance beyond the implementation of contact tracing. Overall, our study yields a number of empirical insights that contribute to enlarge the knowledge on Covid-related apps: (1) Developer communities contributed rapidly to the COVID-19, with dedicated apps released as early as January 2020; (2) Covid-related apps deliver digital tools to users (e.g., health diaries), serve to broadcast information to users (e.g., spread statistics), and collect data from users (e.g., for tracing); (3) Covid-related apps are less complex than standard apps; (4) they generally do not seem to leak sensitive data; (5) in the majority of cases, Covid-related apps are released by entities with past experience on the market, mostly official government entities or public health organizations.

Malicious Application Detection and Classification System for Android Mobiles

2020 ◽  
pp. 122-142
Author(s):  
Sapna Malik ◽  
Kiran Khatter
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
Android Permission ◽  
Google Play

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

Malicious Application Detection and Classification System for Android Mobiles

2018 ◽  
Vol 9 (1) ◽  
pp. 95-114 ◽  
Author(s):  
Sapna Malik ◽  
Kiran Khatter
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Android Applications ◽  
Android Market ◽  
Malicious Apps ◽  
Android Permission ◽  
Google Play

The Android Mobiles constitute a large portion of mobile market which also attracts the malware developer for malicious gains. Every year hundreds of malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.

Machine Learning Based Malicious Android Application Detection

2020 ◽  
Vol 17 (8) ◽  
pp. 3468-3472
Author(s):  
S. L. Jany Shabu ◽  
Rohan Loganathan Reddy ◽  
V. Maria Anu ◽  
L. Mary Gladence ◽  
J. Refonaa
Keyword(s):  
Machine Learning ◽  
Mobile Application ◽  
Learning Algorithms ◽  
Identity Theft ◽  
Machine Learning Algorithms ◽  
Android Application ◽  
Sensitive Data ◽  
Android Applications ◽  
Malicious Apps

The ultimate aim of the project is to improve permission for detecting the malicious android mobile application using machine learning algorithms. In recent years, the usages of smartphones are increasing steadily and also growth of Android application users are increasing. Due to growth of Android application users, some intruders are creating malicious android applications as a tool to steal the sensitive data and identity theft/fraud mobile bank, mobile wallets. There are so many malicious applications detection tools and software are available. But an effectiveness of malicious applications detection tools is the need for the hour. They are needed to tackle and handle new complex malicious apps created by intruder or hackers.

Android Application Security

2019 ◽  
pp. 46-67 ◽  
Author(s):  
Marwan Omar ◽  
Derek Mohammed ◽  
Van Nguyen ◽  
Maurice Dawson ◽  
Mubarak Banisakher
Keyword(s):  
Central Issue ◽  
Financial Loss ◽  
Sensitive Data ◽  
Confidential Data ◽  
Android Market ◽  
Malicious Apps ◽  
Android Permission ◽  
Monitoring Application ◽  
Free Open Source ◽  
Installation Time

Android is a free, open source platform that allows any developer to submit apps to the Android Market with no restrictions. This enables hackers to pass their malicious apps to the Android Market as legitimate apps. The central issue lies at the heart of the Android permission mechanism, which is not capable of blocking malicious apps from accessing sensitive phone resources (e.g., contact info and browsing history); it either allows or disallows apps from accessing the resources requested by the app at the installation time. This chapter investigated the scope of this issue and concluded that hackers use malicious apps as attack vectors to compromise Android smartphones and steal confidential data and that no security solutions exist to combat malicious apps. The researcher suggested designing a real time monitoring application to detect and deter malicious apps from compromising users' sensitive data; such application is necessary for Android users to protect their privacy and prevent financial loss.

Android Application Security

2021 ◽  
pp. 610-625
Author(s):  
Marwan Omar ◽  
Derek Mohammed ◽  
Van Nguyen ◽  
Maurice Dawson ◽  
Mubarak Banisakher
Keyword(s):  
Central Issue ◽  
Financial Loss ◽  
Sensitive Data ◽  
Confidential Data ◽  
Android Market ◽  
Malicious Apps ◽  
Android Permission ◽  
Monitoring Application ◽  
Free Open Source ◽  
Installation Time

Android is a free, open source platform that allows any developer to submit apps to the Android Market with no restrictions. This enables hackers to pass their malicious apps to the Android Market as legitimate apps. The central issue lies at the heart of the Android permission mechanism, which is not capable of blocking malicious apps from accessing sensitive phone resources (e.g., contact info and browsing history); it either allows or disallows apps from accessing the resources requested by the app at the installation time. This chapter investigated the scope of this issue and concluded that hackers use malicious apps as attack vectors to compromise Android smartphones and steal confidential data and that no security solutions exist to combat malicious apps. The researcher suggested designing a real time monitoring application to detect and deter malicious apps from compromising users' sensitive data; such application is necessary for Android users to protect their privacy and prevent financial loss.

scholarly journals DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis

2020 ◽  
Vol 10 (11) ◽  
pp. 3978
Author(s):  
Xin Su ◽  
Lijun Xiao ◽  
Wenjia Li ◽  
Xuchong Liu ◽  
Kuan-Ching Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Behavior Analysis ◽  
High Performance ◽  
Behavioral Characteristics ◽  
Random Forest Algorithm ◽  
Sensitive Data ◽  
Android Malware ◽  
Security Issues ◽  
Android Applications ◽  
Security Incidents

Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of malicious Android applications have become a hot topic. However, the behavioral portrait of Android malware that can depict the behavior of Android malware is not approached in previous literature. To fill this gap, we propose DroidPortrait, an Android malware multi-dimensional behavioral portrait construction approach. We take the behavior of Android malware as an entry point and extract an informative behavior dataset that includes static and dynamic behavior from Android malware. Next, aiming at Android malware that contains different kinds of behaviors, a behavioral tag is defined then combined with a machine learning (ML) algorithm to implement the correlation of these behavioral tags. Android malware behavioral portrait architecture based on behavior analysis and its design is investigated, as also an optimized random forest algorithm is conceived then combined with Android malware behavioral portrait to detect Android malware. The evaluation findings indicate the DroidPortrait can depict behavioral characteristics of Android malware comprehensive and detect them with high performance.

scholarly journals MADFU: An Improved Malicious Application Detection Method Based on Features Uncertainty

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 792
Author(s):  
Hongli Yuan ◽  
Yongchuan Tang
Keyword(s):  
False Positive Rate ◽  
Regression Function ◽  
Detection Accuracy ◽  
Detection Model ◽  
Android Apps ◽  
Positive Rate ◽  
Android App ◽  
Android Applications ◽  
Malicious Apps ◽  
Static Detection

Millions of Android applications (apps) are widely used today. Meanwhile, the number of malicious apps has increased exponentially. Currently, there are many security detection technologies for Android apps, such as static detection and dynamic detection. However, the uncertainty of the features in detection is not considered sufficiently in these technologies. Permissions play an important role in the security detection of Android apps. In this paper, a malicious application detection model based on features uncertainty (MADFU) is proposed. MADFU uses logistic regression function to describe the input (permissions) and output (labels) relationship. Moreover, it uses the Markov chain Monte Carlo (MCMC) algorithm to solve features’ uncertainty. After experimenting with 2037 samples, for malware detection, MADFU achieves an accuracy of up to 95.5%, and the false positive rate (FPR) is 1.2%. MADFU’s Android app detection accuracy is higher than the accuracy of directly using 24 dangerous permission. The results also indicate that the method for an unknown/new sample’s detection accuracy is 92.7%. Compared to other state-of-the-art approaches, the proposed method is more effective and efficient, by detecting malware.

scholarly journals Selecting Critical Data Flows in Android Applications for Abnormal Behavior Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Pengbin Feng ◽  
Jianfeng Ma ◽  
Cong Sun
Keyword(s):  
Abnormal Behavior ◽  
User Privacy ◽  
Sensitive Data ◽  
Static And Dynamic Analysis ◽  
Behavior Detection ◽  
Critical Data ◽  
Abnormal Behavior Detection ◽  
Data Flows ◽  
Android Applications ◽  
Malicious Apps

Nowadays, mobile devices are widely used to store and process user privacy and confidential data. With the popularity of Android platform, the cases of attacks against users’ privacy-sensitive data within Android applications are on the rise. Researchers have developed sophisticated static and dynamic analysis tools to detect information leakage. These methods cannot distinguish legitimate usage of sensitive data in benign apps from the intentional sensitive data leakages in malicious apps. Recently, malicious apps have been found to treat sensitive data differently from benign apps. These differences can be used to flag malicious apps based on their abnormal data flows. In this paper, we further find that some sensitive data flows show great difference between benign apps and malware. We can use these differences to select critical data flows. These critical flows can guide the identification of malware based on the abnormal usage of sensitive data. We present SCDFLOW, a tool that automatically selects critical data flows within Android applications and takes these critical flows as feature for abnormal behavior detection. Compared with MUDFLOW, SCDFLOW increases the true positive rate of malware detection by 5.73%~9.07% on different datasets and causes an ignorable effect on memory consumption.

EDUKASI TERHADAP REMAJA DALAM PENINGKATAN PENGETAHUAN TENTANG PENGGUNAAN GAME DENGAN BIJAK

2020 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Ikhsan Fuady ◽  
Rangga Saptya MP
Keyword(s):  
Side Effects ◽  
Violent Behavior ◽  
Daily Lives ◽  
Before And After ◽  
Improper Use ◽  
Effective Role ◽  
Education And Outreach ◽  
The Right ◽  
Dedicated Team ◽  
User Ratings

<p align="center"><strong>Abstrak</strong></p><p><em>Pemanfaatan game dikalangan remaja memiliki peran yang efektif sebagai wadah untuk hiburan. Tetapi pemanfaatan game tidak tepat memiliki efek samping game terhadap kehidupan sehari hari remaja, mulai dari kurang bersosialIsasi hingga perilaku kekerasan dikalangan remaja. Penyuluhan terhadap remaja bertujuan untuk memberikan pemahaman kepada remaja tentang variasi game berdasarkan rating pengguna, maupun cerdas dalam manajemen penggunaan game dalam kehidupan remaja sehari hari. Pengetahuan remaja tentang varian/ragam game berdasarkan rating relatif rendah sebagaian besar pemengetahuannya tersebar pada kategori sangat rendah dan rendah yaitu sebesar 65 persen. Metode edukasi dan sosialisasi ini adalah dengan beberapa tahapan. Tahap pertaman tim pengabdian memberikan edukasi dan diskusi tentang beragam bentuk game, karakteristik, serta karakteristik pengguna game yang tepat. Selanjutnya beberapa permainan dan kuis untuk meingkatkan literasi remaja tentang pemanfaatan game secara bijak. Kegiatan penyuluhan ini mampu meningkatkan pemahaman para remaja dalam mengenali game yang baik digunakan, hal ini dapat dilihat dari peningkatan pengetahuan remaja relatif signifikan sebelum dan sesudah penyuluhan.</em></p><p><strong>Kata kunci<em>:</em></strong><strong><em> </em></strong><strong><em>Edukasi, Game, Penyuluhan </em></strong></p><p align="center"><em> </em></p><p align="center"><strong>Abstract</strong> </p><p><em>The use of games among teenagers has an effective role as a forum for entertainment. But the improper use of games has the side effects of games on the daily lives of adolescents, ranging from lack of socialization to violent behavior in adolescents. Counseling against adolescents aims to provide understanding to adolescents about the variety/variance of games based on user ratings, as well as being smart in managing game use in daily teenage life. Teenagers' knowledge about game variants/based on the rating is relatively low, most of the knowledge is spread in the very low and low categories, which is 65 percent. The method of education and outreach is by several stages. The first stage of the dedicated team provided education and discussion about various forms of games, characteristics, and characteristics of the right game user. Furthermore, some games and quizzes to improve teen literacy about game use wisely. This counseling activity can increase the understanding of teenagers in recognizing games that are well used, this can be seen from the relatively significant increase in adolescent knowledge before and after counseling.</em></p><p><strong>Keywords<em>:</em></strong><em> <strong>Education, Games, Counseling</strong></em></p>

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