Automation of Android applications functional testing using machine learning activities classification

2018 ◽  
Author(s):  
Ariel Rosenfeld ◽  
Odaya Kardashov ◽  
Orel Zang
Keyword(s):  
Machine Learning ◽  
Learning Activities ◽  
Functional Testing ◽  
Android Applications

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.

Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

2019 ◽  
Vol 11 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Kavita Sharma ◽  
B. B. Gupta
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
High Accuracy ◽  
Learning Approaches ◽  
Privacy Risk ◽  
Test Set ◽  
Extensive Test ◽  
Android Applications ◽  
Android Market ◽  
F Measure

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

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.

scholarly journals MEDIA PEMBELAJARAN MENULIS PUISI BERBASIS APLIKASI ANDROID UNTUK SISWA SMA

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Abdul Haris Faisal ◽  
NFn Zuriyati ◽  
Eva Leiliyanti
Keyword(s):  
High School ◽  
Research Method ◽  
Learning Activities ◽  
Instructional Media ◽  
School Subjects ◽  
Poetry Writing ◽  
Android Applications ◽  
Addie Model ◽  
Bahasa Indonesia

Poetry writing activities are still considered difficult by students. The learning media used have not been able to support learning activities on poetry material. Therefore, this study aims to produce  Android-based learning media on poetry material for high school subjects. This research method adapts the ADDIE model to design a system in learning. The instruments in this study used media validation sheets and material validation sheets. The results of this study indicate that the learning media based on an Android applications is declared appropriate for use in learning. The feasibility is evidenced by the result of the assessment of media experts and material experts who state that the learning media is very accomodating and qualified. Based on these result, instructional media for writing poetry is recommended to be develop in other materials in bahasa Indonesia. AbstrakKegiatan menulis puisi masih dianggap sulit oleh siswa. Media pembelajaran yang digunakan selama ini belum mampu menunjang kegiatan pembelajaran pada materi puisi. Oleh karena itu, tujuan penelitian ini adalah untuk menghasilkan sebuah media pembelajaran berbasis aplikasi android pada materi puisi untuk siswa kelas X SMA. Metode penelitian ini mengadaptasi model ADDIE untuk merancang sebuah sistem dalam pembelajaran. Instrumen dalam penelitian ini menggunakan lembar validasi media dan lembar validasi materi. Hasil penelitian ini menunjukkan bahwa media pembelajaran menulis puisi berbasis aplikasi android dinyatakan layak digunakan dalam pembelajaran. Kelayakan tersebut dibuktikan dari hasil penilaian pakar media dan pakar materi yang menyatakan bahwa media pembelajaran  sangat  akomodatif dan berkualitas. Berdasarkan hasil tersebut, media pembelajaran menulis puisi berbasis aplikasi direkomendasikan untuk dikembangkan pada materi lainnya dalam mata pelajaran bahasa Indonesia. 

scholarly journals A Neural Network Based Hybrid Approach for Analysing and Detecting Malware Threat in Android Applications

2018 ◽  
Vol 7 (4.6) ◽  
pp. 410
Author(s):  
Hetal Suresh ◽  
Joseph Raymond V
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Static Analysis ◽  
Mobile Phones ◽  
Recurrent Neural Network ◽  
Hybrid Approach ◽  
Sensitive Data ◽  
Social Accounts ◽  
Android Applications ◽  
Do So

Mobile phones has become very integral part in our day to day life. In the digitalized world most of our day to day activities rely on mobile phone like banking activities, wallet payments, credentials, social accounts etc. Our system works in such a way that if there is an advantage to a technology there also exists a disadvantage. Every users have all their private and sensitive data in their mobile phones and download random applications from different platforms like play store, App store etc. There is a huge possibility that the applications downloaded are malicious applications. The existing system provides a solution for detection of such applications with the help of antivirus which has pre-built signatures that can be used to obtain an already existing malware which can be modified and manipulated by the hacker if they tend to do so. In this project, our purpose is to identify the malicious applications using Machine learning. By combining both static analysis and dynamic analysis we can use a Hybrid approach for analysing and detecting malware threats in android applications using Recurrent Neural Network (RNN). The main aim of this project will be to ensure that the application installed is benign, if it is not, it should block such applications and notify the user. 

scholarly journals MEDIA PEMBELAJARAN MENULIS PUISI BERBASIS APLIKASI ANDROID UNTUK SISWA SMA

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Abdul Haris Faisal ◽  
NFn Zuriyati ◽  
Eva Leiliyanti
Keyword(s):  
High School ◽  
Research Method ◽  
Learning Activities ◽  
Instructional Media ◽  
School Subjects ◽  
Poetry Writing ◽  
Android Applications ◽  
Addie Model ◽  
Bahasa Indonesia

Poetry writing activities are still considered difficult by students. The learning media used have not been able to support learning activities on poetry material. Therefore, this study aims to produce  Android-based learning media on poetry material for high school subjects. This research method adapts the ADDIE model to design a system in learning. The instruments in this study used media validation sheets and material validation sheets. The results of this study indicate that the learning media based on an Android applications is declared appropriate for use in learning. The feasibility is evidenced by the result of the assessment of media experts and material experts who state that the learning media is very accomodating and qualified. Based on these result, instructional media for writing poetry is recommended to be develop in other materials in bahasa Indonesia. AbstrakKegiatan menulis puisi masih dianggap sulit oleh siswa. Media pembelajaran yang digunakan selama ini belum mampu menunjang kegiatan pembelajaran pada materi puisi. Oleh karena itu, tujuan penelitian ini adalah untuk menghasilkan sebuah media pembelajaran berbasis aplikasi android pada materi puisi untuk siswa kelas X SMA. Metode penelitian ini mengadaptasi model ADDIE untuk merancang sebuah sistem dalam pembelajaran. Instrumen dalam penelitian ini menggunakan lembar validasi media dan lembar validasi materi. Hasil penelitian ini menunjukkan bahwa media pembelajaran menulis puisi berbasis aplikasi android dinyatakan layak digunakan dalam pembelajaran. Kelayakan tersebut dibuktikan dari hasil penilaian pakar media dan pakar materi yang menyatakan bahwa media pembelajaran  sangat  akomodatif dan berkualitas. Berdasarkan hasil tersebut, media pembelajaran menulis puisi berbasis aplikasi direkomendasikan untuk dikembangkan pada materi lainnya dalam mata pelajaran bahasa Indonesia. 

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