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Android Malware Detection based on Factorization Machine [article]

Chenglin Li, Keith Mills, Rui Zhu, Di Niu, Hongwen Zhang, Husam Kinawi
2019 arXiv   pre-print
In this paper, we propose a novel and highly reliable classifier for Android Malware detection based on a Factorization Machine architecture and the extraction of Android app features from manifest files  ...  These metrics match the performance of state-of-the-art machine-learning-based Android malware detection methods and several commercial antivirus engines with the benefit of training up to 50 times faster  ...  EXPERIMENTS In this section, we evaluate the performance of our Factorization Machine-based Android malware detection system.  ... 
arXiv:1805.11843v2 fatcat:k3xllbxtqbf7pfzm5wdkwclyjy

A Review of Android Malware Detection Approaches based on Machine Learning

Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, Haifeng Liu
2020 IEEE Access  
This paper presents a comprehensive survey of Android malware detection approaches based on machine learning.  ...  This review will help academics gain a full picture of Android malware detection based on machine learning.  ...  Some previous studies have discussed Android malware detection approaches based on machine learning.  ... 
doi:10.1109/access.2020.3006143 fatcat:5rn2qg67ezdixkrefwxmyejhsi

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  
It also addresses malware detection methods.  ...  Identification of Android OS malware has become an emerging research subject of concern. This paper aims to analyze the various characteristics involved in malware detection.  ...  Compared to traditional techniques, such as signature-based malware detection, machine learning-based recognition, which is based on the detection of unusual characteristics in identified malware, can  ... 
doi:10.9734/ajrcos/2021/v7i430189 fatcat:jgiqsg4nxbhsnal5wtw6k4dije

Survey on Android Malware Detection Using Multilevel Classifier Fusion

Mahesh R. Gawale
2019 International Journal for Research in Applied Science and Engineering Technology  
The purpose of this work is the improved malware and benign detection accuracy based on machine learning algorithms.  ...  Android Malware detection is a very important factor in security of the smartphones and computer systems.  ...  Android malware detection is based on the static analysis and dynamic analysis.  ... 
doi:10.22214/ijraset.2019.3346 fatcat:vdgz5c7tdzadrh4fyloevoyvnu

Android Malware Detection Using Deep Learning

Omar N. Elayan, Ahmad M. Mustafa
2021 Procedia Computer Science  
Traditional Android malware detection methods, such as signaturebased methods or methods monitoring battery consumption, may fail to detect recent malware.  ...  Traditional Android malware detection methods, such as signaturebased methods or methods monitoring battery consumption, may fail to detect recent malware.  ...  [18] proposed a classification approach based on parallel machine learning to detect Android malware.  ... 
doi:10.1016/j.procs.2021.03.106 fatcat:6usxx4cbcndyvemkqb6uttxg4a

An Enhanced Novel GA-based Malware Detection in End Systems Using Structured and Unstructured Data by Comparing Support Vector Machine and Neural Network

Sai Tejeshwar Reddy T
2021 Revista GEINTEC  
For each algorithm take N=10 samples from the dataset collected and perform two iterations on each algorithm to identify the Malware Detection.  ...  Result: The accuracy results of the Neural Network model has potential up to (82.91%) and the Support Vector Machine algorithm has an accuracy of (79.67%) for Android malware detection with the significance  ...  In study of all research papers the best paper is An Android malware detection system based on machine learning (Wen and Yu 2017) Previously our team has a rich experience in working on various research  ... 
doi:10.47059/revistageintec.v11i2.1777 fatcat:mzkdyks3avdlrnhb7y7yzac42u

Fuzzy Integral-Based Multi-Classifiers Ensemble for Android Malware Classification

Altyeb Taha, Omar Barukab, Sharaf Malebary
2021 Mathematics  
The experimental results using the dataset, consisting of 9476 Android goodware apps and 5560 malware Android apps, show that the proposed approach for Android malware classification based on the Choquet  ...  This makes effective detection of Android malware apps a difficult problem and important issue.  ...  Peiravian and Zhu [28] introduced a machine-learning-based model for Android malware detection.  ... 
doi:10.3390/math9222880 fatcat:svmv3ppkifgg3eqfohxp3h462q

On Malware Detection on Android Smartphones

Eman Shalabi
2020 International Journal for Research in Applied Science and Engineering Technology  
In this paper, we discuss various mobile malware types and datasets used for mobile malware detection process. We also survey various mobile malware detection techniques.  ...  The large increase in the use of smartphones leads to a large increase in generating mobile malware.  ...  EnDroid detects malware from android applications based on multiple types of dynamic behavioral features.  ... 
doi:10.22214/ijraset.2020.6160 fatcat:dv2k5iojjnb6bc3wzlkdurasom

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  
However, the most recent Android malware apps, such as zero-day, cannot be detected through conventional methods that are still based on fixed signatures or identifiers.  ...  In response, specific tools and anti-virus programs used conventional signature-based methods in order to detect such Android malware applications.  ...  Alternatively, numerous research works [8] [9] [4] [10] focused on training machine learning classification algorithms based on known Android malware apps in order to detect unknown Android malware  ... 
doi:10.14569/ijacsa.2020.0110429 fatcat:q6au2thucjhjfny3el5vwhhdqy

Malware Detection & Prevention in Android Mobile by using Significant Permission Identification & Machine Learning

Ms. Kirti Reddy
2020 International Journal for Research in Applied Science and Engineering Technology  
Malware is today one of the biggest security threat to internet.  ...  Our method defines the substantial work permission needed by an application and differentiate between essential and non-essential permissions and detect and remove the malware on such a basis.  ...  As a result, researchers use techniques of machine learning and data mining to detect Android malware based on use of permission.  ... 
doi:10.22214/ijraset.2020.6046 fatcat:atbikmtpjbhdxnlkowshtbjq6a

A Survey of Android Malware Static Detection Technology Based on Machine Learning

Qing Wu, Xueling Zhu, Bo Liu
2021 Mobile Information Systems  
To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage  ...  In this paper, we investigated Android applications' structure, analysed various sources of static features, reviewed the machine learning methods for detecting Android malware, studied the advantages  ...  Our work aims to provide a comprehensive survey about Android malware static detection based on machine learning technologies.  ... 
doi:10.1155/2021/8896013 doaj:9dc548d197fd404fbcd4ee962f374bde fatcat:mbuavifbmzfmjm3shzm4wcbm4a

Host-Based Detection and Analysis of Android Malware

Moses Ashawa, Sarah Morris
2019 International Journal for Information Security Research  
The result calls proactive measures rather than proactive in tackling malware infection on Android based mobile devices.  ...  The Rapid expansion of mobile Operating Systems has created a proportional development in Android malware infection targeting Android which is the most widely used mobile OS. factors such Android open  ...  This paper proposed a host-based detection of android malware by creating Linux Malware Detect rules on the virtual machine.  ... 
doi:10.20533/ijisr.2042.4639.2019.0100 fatcat:7zqrmcrm3fd5bk5zaiser7jgjq

A state-of-the-art survey of malware detection approaches using data mining techniques

Alireza Souri, Rahil Hosseini
2018 Human-Centric Computing and Information Sciences  
In Fig. 1 , we illustrate a malware detection taxonomy based on machine learning approaches.  ...  detection analyses using meta-heuristic algorithms can influence the speed up of the execution time and the total accuracy factor of the data mining process. • Real-time malware detection: Is based on  ... 
doi:10.1186/s13673-018-0125-x fatcat:hagxzdxmczfe7eky5fqdgjedli

Android Malware Detection Based on Feature Selection and Weight Measurement

Huizhong Sun, Guosheng Xu, Zhimin Wu, Ruijie Quan
2022 Intelligent Automation and Soft Computing  
The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly  ...  In detecting Android malware samples, the proposed method can achieve an accuracy of 99% and an F1-score of 98%.  ...  Whether based on static analysis-based malware detection or dynamic analysis-based malware detection, application API calls is a key factor. For example, Wu et al.  ... 
doi:10.32604/iasc.2022.023874 fatcat:pp3jraesajevzi7zg5qgmu6jau

Android Malware Detection based on Vulnerable Feature Aggregation

Arindaam Roy, Divjeet Singh Jas, Gitanjali Jaggi, Kapil Sharma
2020 Procedia Computer Science  
In this paper, we present a novel feature-engineering technique for android malware detection using Machine Learning.  ...  In this paper, we present a novel feature-engineering technique for android malware detection using Machine Learning.  ...  [13] proposed a deep belief network based Android malware detection system DroidDelver.  ... 
doi:10.1016/j.procs.2020.06.040 fatcat:kw7men45dbaqpmwpnuly6r2riq
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