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An Enhanced Method for Identifying Android Malware Detection

2019 International journal of recent technology and engineering  
The existing method, "Significant Permission Identification for Machine-Learning-Based Android Malware Detection (SIGPID)", which uses Multi-Level Data Pruning process to identify significant permissions  ...  To reduce features of malicious apps further, an enhanced method called "Enhanced Model of Significant Permission Identification (ESID)" is proposed to identify android malware applications using data  ...  The existing method, "Significant Permission Identification for Machine-Learning-Based Android Malware Detection (SIGPID)", which uses Multi-Level Data Pruning process to identify significant permissions  ... 
doi:10.35940/ijrte.d5307.118419 fatcat:6isqqm5cyjepzb3wxk5he232q4

A Comprehensive Study of Malware Detection in Android Operating Systems

Suhaib Jasim Hamdi, Ibrahim Mahmood Ibrahim, Naaman Omar, Omar M. Ahmed, Zryan Najat Rashid, Awder Mohammed Ahmed, Rowaida Khalil Ibrahim, Shakir Fattah Kak, Hajar Maseeh Yasin, Azar Abid Salih
2021 Asian Journal of Research in Computer Science  
This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices.  ...  This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static.  ...  The results suggested that permission data is better for malware detection than system call data [79] . Their paper is based on Android and learning machines.  ... 
doi:10.9734/ajrcos/2021/v10i430248 fatcat:wkxtllw4efaexpkrhgg3uwzdne

A State of Art Survey for Understanding Malware Detection Approaches in Android Operating System

Suhaib Jasim Hamdi, Naaman Omar, Adel AL-zebari, Karwan Jameel Merceedi, Abdulraheem Jamil Ahmed, Nareen O. M. Salim, Sheren Sadiq Hasan, Shakir Fattah Kak, Ibrahim Mahmood Ibrahim, Hajar Maseeh Yasin, Azar Abid Salih
2021 Asian Journal of Research in Computer Science  
Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning.  ...  Existing research suggests that machine learning is an effective and promising way to detect Android malware.  ...  The results suggested that permission data is better for malware detection than system call data [97] . Their paper is based on Android and learning machines.  ... 
doi:10.9734/ajrcos/2021/v11i330266 fatcat:fcaiwoexh5hk7dce6triq3eu5y

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  
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.  ...  We need a robust malware detection solution to counter this serious malware project that can effectively and efficiently recognis e malware apps.  ...  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

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  
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  ...  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.  ...  The Artificial Introduction Methods of knowledge like machine learning are very much Enhanced opportunities for Android malware detection. parameters as well as different types of Android malware identification  ... 
doi:10.9734/ajrcos/2021/v7i430189 fatcat:jgiqsg4nxbhsnal5wtw6k4dije

A Survey on Permission based Malware Detection in Android Applications

Sunali Jogsan, Gujarat Technological University - Graduate School of Engineering Technology
2020 International Journal of Engineering Research and  
So detecting the malicious android application is a must.  ...  The android operating system is basically for mobiles and is quickly gaining the market share, where most of the smartphones and tablets either released or set to be released.  ...  Hence there are several proposed methods available for permission-based mobile malware detection systems using Machine Learning.  ... 
doi:10.17577/ijertv9is040774 fatcat:omddrxxr3nhovgwbz4qpiofgtq

Permission Extraction Framework for Android Malware Detection

Ali Ghasempour, Nor Fazlida, Ovye John
2020 International Journal of Advanced Computer Science and Applications  
In this work, the researchers proposed a multi-level permission extraction framework for malware detection in an Android device.  ...  Nowadays, Android-based devices are more utilized than other Operating Systems based devices.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the Universiti Putra Malaysia for supporting this research.  ... 
doi:10.14569/ijacsa.2020.0111159 fatcat:2iqfq7rlvbb3fc6xnlvjuepw6m

Android Malicious Application Classification Using Clustering [article]

Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi, Mohit Sewak
2019 arXiv   pre-print
Hence time-to-time various authors have proposed different machine learning solutions to identify sophisticated malware.  ...  It appears that most of the anti-malware still relies on the signature-based detection system which is generally slow and often not able to detect advanced obfuscated malware.  ...  Li et al. used permission as the feature vector and used three level pruning for identification of significant permission for effective detection of malware and benign [20] .  ... 
arXiv:1904.10142v1 fatcat:4wwzlbxnkncoxomhceatuwjktq

A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features

Rajesh Kumar, Xiaosong Zhang, Wen Yong Wang, Riaz Ullah Khan, Jay Kumar, Abubaker Sharif
2019 IEEE Access  
INDEX TERMS Android malware detection, blockchain, Internet of Things (IoT), clustering, secure machine learning.  ...  This paper presents a novel framework that combines the advantages of both machine learning techniques and blockchain technology to improve the malware detection for Android IoT devices.  ...  Machine learning was used to detect and train the model for Android malware detection, creating a vast amount of malware database.  ... 
doi:10.1109/access.2019.2916886 fatcat:2ty3jo6hszhbfezp3qa7lapn6m

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  ...  Antiviruses software that still relies on a signature-based database that is effective only in identifying known malware.  ...  Android Malware Detection using Machine Learning Andrew Bedford et al. [51] developed ANDRANA a malware detection approach based on machine learning algorithms.  ... 
arXiv:1904.00736v1 fatcat:hdhohtyvqfdbdoccs45tnxpyea

A Review on The Use of Deep Learning in Android Malware Detection [article]

Abdelmonim Naway, Yuancheng LI
2018 arXiv   pre-print
One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection.  ...  Android is the predominant mobile operating system for the past few years.  ...  [26] designed a deep learning system for Android malware detection. The proposed approach employs static analysis to acquire permission from the apps.  ... 
arXiv:1812.10360v1 fatcat:lncdcxsbyrarnk4pyivus5rn7q

SigPID: significant permission identification for android malware detection

Lichao Sun, Zhiqiang Li, Qiben Yan, Witawas Srisa-an, Yu Pan
2016 2016 11th International Conference on Malicious and Unwanted Software (MALWARE)  
In this thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware.  ...  Based on the identified significant permissions, SigPID utilizes classification algorithms to classify different families of malware and benign apps.  ...  Introducing SigPID: Significant Permission Identification for Android Malware Detection The goal of Significant Permission IDentification (SigPID) system is to achieve high malware detection accuracy  ... 
doi:10.1109/malware.2016.7888730 dblp:conf/malware/SunLYSP16 fatcat:3lm4ahdnrze27h2obkrjrub4ca

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

Using Machine Learning to Identify Android Malware Relying on API calling sequences and Permissions

Eslam Amer, Ammar Mohamed, Seif ElDein Mohamed, Mostafa Ashaf, Amr Ehab, Omar Shereef, Haytham Metwaie
2022 Journal of Computing and Communication (Online)  
In this paper, we introduce an Android malware detection technique based on API and permissions.  ...  We discovered varied performance when we analyses all Android malware detection classifiers that use machine learning, suggesting that machine learning algorithms are effectively utilized in this area  ...  Due to the rapid development of malware, adaptive machine learning methods are used to conduct Android harmful detection.  ... 
doi:10.21608/jocc.2022.218454 fatcat:jnlamw2rtfai7ihck5mxsn27ce

Research on Data Mining of Permission-Induced Risk for Android IoT Devices

Rajesh Kumar, Xiaosong Zhang, Riaz Khan, Abubakar Sharif
2019 Applied Sciences  
The experimental outcomes demonstrate high proficiency of the accuracy for malware detection, which is pivotal for android apps aiming for secure data exchange between IoT devices.  ...  Most of the studies focused on detecting malware based on static and dynamic analysis of the applications. However, to analyse the risky permission at runtime is a challenging task.  ...  Static Analysis The recent use of static features of machine learning to detect Android malware include the following: Cen et al.  ... 
doi:10.3390/app9020277 fatcat:msvyyqj7pjdexlb3g2jjgfwuzm
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