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Automation of Android Applications Testing Using Machine Learning Activities Classification [article]

Ariel Rosenfeld, Odaya Kardashov, Orel Zang
2017 arXiv   pre-print
In this paper, we present a novel approach for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios.  ...  Mobile applications are being used every day by more than half of the world's population to perform a great variety of tasks.  ...  Conclusions This paper introduces a novel approach for testing Android applications using machine learning techniques.  ... 
arXiv:1709.00928v1 fatcat:s2imj7qfoze3toouc57oax7ofq

ACAT: A Novel Machine-Learning-Based Tool for Automating Android Application Testing [chapter]

Ariel Rosenfeld, Odaya Kardashov, Orel Zang
2017 Lecture Notes in Computer Science  
In this demonstration, we present a novel tool for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios.  ...  Mobile applications are being used every day by more than half of the world's population to perform a great variety of tasks.  ...  In order to use this similarity to our benefit, we use machine learning techniques to classify each activity in the application into one of seven pre-defined activity types.  ... 
doi:10.1007/978-3-319-70389-3_14 fatcat:mtql6sms5jghbn2rjz266tabru

DroidAutoML: A Microservice Architecture to Automate the Evaluation of Android Machine Learning Detection Systems [chapter]

Yérom-David Bromberg, Louison Gitzinger
2020 Lecture Notes in Computer Science  
Accordingly, it paves the way to the emergence of new approaches based on Machine Learning (ML) technics to boost the detection of unknown malware variants.  ...  In reality, in the Android community, state-of-the-art studies do not focus on model training, and most often go through an empirical study with a manual process to choose the learning strategy, and/or  ...  Automated Machine Learning Frameworks. Several works already studied automated machine learning as a research problem [30, 34] .  ... 
doi:10.1007/978-3-030-50323-9_10 fatcat:achir3bwyjgcvnffm32oxvnlgu

Automated Feature Selection using Boruta Algorithm to Detect Mobile Malware

2020 International Journal of Advanced Trends in Computer Science and Engineering  
The proposed method adopts machine learning prediction and optimizes the selecting features in order to reduce the model of machine learning complexity.  ...  Boruta algorithm is used to select features automatically for assisting the machine learning.  ...  This paper presented automated feature selection using boruta algorithm and machine learning method to detect and predict mobile malware.  ... 
doi:10.30534/ijatcse/2020/307952020 fatcat:7fmuwmfj2jcftkethu5f4phhyu

Applying machine learning classifiers to dynamic Android malware detection at scale

Brandon Amos, Hamilton Turner, Jules White
2013 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC)  
Machine learning classifiers are a current method for detecting malicious applications on smartphone systems.  ...  This paper presents the evaluation of a number of existing classifiers, using a dataset containing thousands of real (i.e. not synthetic) applications.  ...  There is a clear difference in correct classification percentage of the cross validation set (made up of applications used in training) versus the testing set (made up of applications never used in training  ... 
doi:10.1109/iwcmc.2013.6583806 dblp:conf/iwcmc/AmosTW13 fatcat:nk7j4x6yhvbhfkdpfvjfy2m274

Android Malware Detection through Machine Learning Techniques: A Review

Abikoye Oluwakemi Christiana, Benjamin Aruwa Gyunka, Akande Noah
2020 International Journal of Online and Biomedical Engineering (iJOE)  
This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p>  ...  The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown  ...  A framework for the detection of Android malicious application that was based on Support Vector Machine (SVM) and Active Learning technologies was proposed in [31] .  ... 
doi:10.3991/ijoe.v16i02.11549 fatcat:hurc3snq6zgzzmrbnhvzafxvxu

Machine learning aided Android malware classification

Nikola Milosevic, Ali Dehghantanha, Kim-Kwang Raymond Choo
2017 Computers & electrical engineering  
In this paper, we present two machine learning aided approaches for static analysis of the mobile applications: one based on permissions , while the other based on source code analysis that utilizes a  ...  Our source code based classification achieved F-score of 95.1%, while the approach that used permission names only performed with F-measure of 89%.  ...  Our approach is, to the best of our knowledge, the only automated static malware analysis method for android applications that uses machine learning.  ... 
doi:10.1016/j.compeleceng.2017.02.013 fatcat:gc2za6kf3fc5jfvnfize6sltxi

Using Deep Neural Network for Android Malware Detection [article]

Abdelmonim Naway, Yuancheng LI
2019 arXiv   pre-print
To contrive with malicious applications that are increased in volume and sophistication, we propose an Android malware detection system that applies deep learning technique to face the threats of Android  ...  The pervasiveness of the Android operating system, with the availability of applications almost for everything, is readily accessible in the official Google play store or a dozen alternative third-party  ...  This section reviews some of the presently concerned works of machine learning and deep learning for static analysis and dynamic analysis in Android malware detection and classification. 1.  ... 
arXiv:1904.00736v1 fatcat:hdhohtyvqfdbdoccs45tnxpyea

Advances in test automation for software with special focus on artificial intelligence and machine learning

J . Jenny Li, Andreas Ulrich, Xiaoying Bai, Antonia Bertolino
2019 Software quality journal  
The second paper "Virtualization of Stateful Services via Machine Learning" proposes an approach to create stateful service mocks using two different machine learning techniques to support testing, which  ...  The comparison included MINT that is an EFSM inference tool, adopting the classification and sequence-tosequence-based machine learning algorithms.  ... 
doi:10.1007/s11219-019-09472-3 fatcat:wn6ijk67tja5fgjonkukm7rr5m

Detection of Android Adwares by using Machine Learning Algorithms

2020 International Journal of Engineering and Advanced Technology  
In this paper, we analyze the pertinence of machine learning based solutions to detect android malware, particularly Adware.  ...  Scenario A for binary classification and Scenario B is for multi-class classification. The 60% of the dataset is used to train the ML algorithms and the remaining 40% is reserved for the testing.  ...  Several of Machine Learning (ML) based algorithms have been used by academicians and researchers to study their applicability in anomaly-based malware detection solutions.  ... 
doi:10.35940/ijeat.d1005.0484s19 fatcat:l7ygijynujc6dmapa5rigs6644

Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

Talal A.A Abdullah, Waleed Ali, Rawad Abdulghafor
2020 International Journal of Advanced Computer Science and Applications  
The increasing number of mobile devices using the Android operating system in the market makes these devices the first target for malicious applications.  ...  Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing  ...  An automated testing tool called WaffleDetector was implemented by [20] to identify Android malware by proposing a group of Android features consisting of sensitive permissions and API to feed machine  ... 
doi:10.14569/ijacsa.2020.0110429 fatcat:q6au2thucjhjfny3el5vwhhdqy

MobiGUITAR: Automated Model-Based Testing of Mobile Apps

Domenico Amalfitano, Anna Rita Fasolino, Porfirio Tramontana, Bryan Dzung Ta, Atif M Memon
2015 IEEE Software  
A number of bugs are "Android-specific," stemming from the event-and activity-driven nature of Android.  ...  We feel that these tools are inappropriate for amateur programmers, an increasing fraction of the app developer population. We present MobiGUITAR for automated GUI-driven testing of Android apps.  ...  Some bugs showed us that Android applications may have incorrect behaviors due to a wrong management of the lifecycle of their Activities.  ... 
doi:10.1109/ms.2014.55 fatcat:j6p67scsujcufnnqr7dmkiawpe

Modern Extensions to Hospital Information Systems

Varun Jain, Rishabh Dave, Shiwani Gupta
2017 International Journal of Computer Applications  
of managing, processing and learning from the data, keeping in mind the sustainable modern technologies available for automation and machine learning.  ...  The aim of this paper is to inform both healthcare practitioners and software solutions creators about the ways in which the Hospital Information Systems (HIS) can and should be extended, both in terms  ...  Integrating with HIS Three use cases were covered while testing an implementation during the writing of this research paper. First was to use an Android application for outpatients.  ... 
doi:10.5120/ijca2017914092 fatcat:buf2pqamd5dtpi6ml4azb7qvf4

A Literature Review on Software Testing Techniques for Smartphone Applications

M. N. A. Khan, A. M. Mirza, R. A. Wagan, M. Shahid, I. Saleem
2020 Engineering, Technology & Applied Science Research  
To produce a quality app, developers and testers need to test and assess the app in numerous ways to ensure the best trait of the application.  ...  In this concern, some efficient and mature techniques are required to test smartphone applications.  ...  -Silverlight -News reader -Spreadsheet applications -Advertising -Mobile learning -Sales force automation -Android testing -Usability testing -Security testing -Test automation -Context  ... 
doi:10.48084/etasr.3844 fatcat:puf65a4ornhgxbrqdmn55vo3wy

Self-reported activities of Android developers

Luca Pascarella, Franz-Xaver Geiger, Fabio Palomba, Dario Di Nucci, Ivano Malavolta, Alberto Bacchelli
2018 Proceedings of the 5th International Conference on Mobile Software Engineering and Systems - MOBILESoft '18  
machine learning techniques.  ...  with machine learning techniques.  ...  Automated classification of activities With our final research question we test standard machine learning techniques to automatically classify self-reported activities.  ... 
doi:10.1145/3197231.3197251 dblp:conf/icse/PascarellaGPNMB18 fatcat:gv64bcf5hza6hnavc7fnbed5bu
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