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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

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

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  ...  Fig. 2 describes the structure of the deep neural network. Relu and softmax are used as activation functions. Gradient Descent is used for optimization.  ... 
arXiv:1904.00736v1 fatcat:hdhohtyvqfdbdoccs45tnxpyea

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

A New Android Malware Detection Approach Using Bayesian Classification

S. Y. Yerima, S. Sezer, G. McWilliams, I. Muttik
2013 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA)  
The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities.  ...  A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners.  ...  ease compared to other machine learning techniques.  ... 
doi:10.1109/aina.2013.88 dblp:conf/aina/YerimaSMM13 fatcat:bniryqfsyraypiu24cjqbjvdru

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

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  ...  Health Informatics functionally depends on the field of computing, specifically Software Engineering, Data Science and Machine Learning.  ... 
doi:10.5120/ijca2017914092 fatcat:buf2pqamd5dtpi6ml4azb7qvf4

Automated Test Selection for Android Apps Based on App Domain

Luca Ardito, Riccardo Coppola, Simone Leonardi, Maurizio Morisio, Ugo Buy
2020 IEEE Access  
work on machine learning strategies to classify Android app.  ...  ACTIVITY CLASSIFICATION Logistic regression is the best performing machine learning algorithm in activity classification among the algorithms we tested, as shown in Table 12 .  ...  SCRIPTS FOR TESTING FRAMEWORK EVALUATION  ... 
doi:10.1109/access.2020.3029735 fatcat:dqqcwzrzfzcrxhdr54bjb2vycu

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

Efficiency of Malware Detection in Android System: A Survey

Maria A. Omer, Subhi R. M. Zeebaree, Mohammed A. M. Sadeeq, Baraa Wasfi Salim, Sanaa x Mohsin, Zryan Najat Rashid, Lailan M. Haji
2021 Asian Journal of Research in Computer Science  
Android OS is wide-ranging in the mobile industry today because of its open-source architecture. It is a wide variety of applications and basic features.  ...  The current detection mechanism utilizes algorithms such as Bayesian algorithm, Ada grad algorithm, Naïve Bayes algorithm, Hybrid algorithm, and other algorithms for machine learning to train the sets  ...  different machine-learning (ML) classifications.  ... 
doi:10.9734/ajrcos/2021/v7i430189 fatcat:jgiqsg4nxbhsnal5wtw6k4dije

Analysis of Bayesian classification-based approaches for Android malware detection

Suleiman Y. Yerima, Gavin McWilliams, Sakir Sezer
2014 IET Information Security  
Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis.  ...  Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware  ...  Keywords mobile security, Android, malware detection, data mining, Bayesian classification, static analysis, machine learning. 1.  ... 
doi:10.1049/iet-ifs.2013.0095 fatcat:lqahojqfonfrzc4ir7n4kr7ndm

Visual Detection for Android Malware using Deep Learning

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Then we used the deep learning technique convolutional neural network to identify Android application as benign or as malware.  ...  According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively.  ...  Email: 0x4186@gmail.com use the deep learning algorithm for classification android applications.  ... 
doi:10.35940/ijitee.a8132.1110120 fatcat:ejoz2kgpnjcohaiwhoyzzaf7zi

PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting

Ajit Kumar, Vinti Agarwal, Shishir Kumar Shandilya, Andrii Shalaginov, Saket Upadhyay, Bhawna Yadav
2020 Future Internet  
An earlier work named PACE (Platform for Android Malware Classification and Performance Evaluation), was introduced as a unified solution to offer open and easy implementation access to several machine-learning-based  ...  Multiple solutions leveraging big data and machine-learning capabilities to detect Android malware are being constantly developed.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi12040066 fatcat:kwqm4syf5rcspjtpczrjetdkk4

Building malware classificators usable by State security agencies

David Esteban Useche-Peláez, Daniel Orlando Díaz-López, Daniela Sepúlveda-Alzate, Diego Edison Cabuya-Padilla
2018 Iteckne  
A proposal of architecture for an IoT sentinel that uses one of the developed machine learning model is also showed.  ...  Finally, different approaches, perspectives, and challenges about the use of sandboxing and machine learning by security teams in State security agencies are also shared.  ...  ACKNOWLEDGMENT This work has been supported partially by the Colombian School of Engineering Julio Garavito (Colombia) through the project "Cyber Security Architecture for Incident Management", funded  ... 
doi:10.15332/iteckne.v15i2.2072 fatcat:nfeiawae5vd4hpci2dufnpeb2y
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