1,443 Hits in 6.8 sec

Permission Based in Android Malware Classification

2018 DEStech Transactions on Engineering and Technology Research  
This project had proposed clustering in intrusion detection method using hybrid learning approaches combining K-Means clustering and Naïve Bayes classification had been proposed.  ...  Android malware is growing in such an exponential pace which lead out for automated tools that can aid the malware analyst in analysing the behaviours of new malicious applications.  ...  Acknowledgement This research was financially supported by the Ministry of Higher Education through Fundamental Research Grant Scheme (500-RMI/FRGS 5/3 0006/2016).  ... 
doi:10.12783/dtetr/ecame2017/18465 fatcat:rcd2kfqkubdxjhvpbuhxbykqcy

Machine learning aided Android malware classification

Nikola Milosevic, Ali Dehghantanha, Kim-Kwang Raymond Choo
2017 Computers & electrical engineering  
Our approach provides a method for automated static code analysis and malware detection with high accuracy and reduces smartphone malware analysis time.  ...  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  ...  Evaluation of permission-based clustering In our case of permission-based analysis, clustering showed higher error rate than classification and it can be hardly used in malware detection.  ... 
doi:10.1016/j.compeleceng.2017.02.013 fatcat:gc2za6kf3fc5jfvnfize6sltxi

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)  
Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based.  ...  This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p>  ...  Classification and clustering are the two machine learning approaches utilized by the Authors but classification method was mostly used for malware detection.  ... 
doi:10.3991/ijoe.v16i02.11549 fatcat:hurc3snq6zgzzmrbnhvzafxvxu

Android Malicious Application Classification Using Clustering [article]

Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi, Mohit Sewak
2019 arXiv   pre-print
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.  ...  Therefore in this paper, we propose a novel scalable and effective clustering method to improve the detection accuracy of the malicious android application and obtained a better overall accuracy (98.34%  ...  For detection of android malware, most of the anti-malware rely on the signature-based detection techniques.  ... 
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  
Android devices and applications are working hand to hand to realize dreams of the IoT. Recently, there is a rapid increase in threats and malware attacks on Android-based devices.  ...  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.  ...  From the machine learningbased methods to the general classification-based methods, various kinds of the Android malware detection methods were studied.  ... 
doi:10.1109/access.2019.2916886 fatcat:2ty3jo6hszhbfezp3qa7lapn6m

Android malware classification based on ANFIS with fuzzy c-means clustering using significant application permissions

2017 Turkish Journal of Electrical Engineering and Computer Sciences  
The proposed approach utilizes the FCM clustering method to determine the optimum number of clusters and cluster centers, which improves the classification accuracy of the ANFIS.  ...  There is an urgent need for efficient and effective Android malware detection techniques.  ...  The authors therefore gratefully acknowledge the DSR technical and financial support.  ... 
doi:10.3906/elk-1602-107 fatcat:wzfvf7evzvbrrotthiqutehvbm

FamDroid: Learning-Based Android Malware Family Classification Using Static Analysis [article]

Wenhao fan, Liang Zhao, Jiayang Wang, Ye Chen, Fan Wu, Yuan'an Liu
2021 arXiv   pre-print
At present, the main problem of existing research works on Android malware family classification lies in that the extracted features are inadequate to represent the common behavior characteristics of the  ...  The malware applications in a malware family may have common features and similar behaviors, which are beneficial for malware detection and inspection.  ...  ACKNOWLEDGMENT This work is supported in part by National Natural Science Foundation of China (Grant No. 61821001), and Fundamental Research Funds for the Central Universities.  ... 
arXiv:2101.03965v2 fatcat:qhe6wrg2rvfkdmjwns5o7uhvwm

Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering [article]

Hemant Rathore, Sanjay K. Sahay, Shivin Thukral, Mohit Sewak
2021 arXiv   pre-print
Therefore in this paper, we propose effective and efficient Android malware detection models based on machine learning and deep learning integrated with clustering.  ...  Also, the segregating of vector space using clustering integrated with Random Forest further boosted the AUC to 99.6% in one cluster and direct detection of Android malware in another cluster, thus reducing  ...  clustering followed by classification further boosted the performance of malware detection models with a higher AUC of 99.6 (using random forest) in one cluster and direct detection of Android malware  ... 
arXiv:2103.00637v1 fatcat:paclfrinbvh6dntjggfzyoun2e

EC2: Ensemble Clustering and Classification for Predicting Android Malware Families

Tanmoy Chakraborty, Fabio Pierazzi, V. S. Subrahmanian
2017 IEEE Transactions on Dependable and Secure Computing  
We present a performance comparison of several traditional classification and clustering algorithms for Android malware family identification on DREBIN, the largest public Android malware dataset with  ...  Experimental results on both the DREBIN and the more recent Koodous malware datasets show that EC2 accurately detects both small and large families, outperforming several comparative baselines.  ...  ACKNOWLEDGMENTS The authors would like to acknowledge the suggestions of the anonymized reviewers.  ... 
doi:10.1109/tdsc.2017.2739145 fatcat:gfjfyb2mevcqnjvrrkffdp4dgq

A Low Computational Cost Method for Mobile Malware Detection Using Transfer Learning and Familial Classification Using Topic Modelling

Saket Acharya, Umashankar Rawat, Roheet Bhatnagar, Shyi-Ming Chen
2022 Applied Computational Intelligence and Soft Computing  
This paper proposes a novel framework for detecting and clustering Android malware using the transfer learning and the topic modelling approach.  ...  The proposed framework provides better accuracy of 98.3% during the classification stage by using the transfer learning approach as compared to other state-of-the-art Android malware detection techniques  ...  Android Malware Detection Based on Sustainability.  ... 
doi:10.1155/2022/4119500 fatcat:a3ybaf7rgrbgvmj22uflgutie4

High Performance Classification of Android Malware Using Ensemble Machine Learning

Pagnchakneat C. Ouk, Wooguil Pak
2022 Computers Materials & Continua  
To protect users from the threats, the solutions to detect and identify the malware variant are essential.  ...  The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number.  ...  We also discuss on the signature-based and machine learning based techniques that use to detect malware application. We then cover about the techniques that focus on malware classification.  ... 
doi:10.32604/cmc.2022.024540 fatcat:ia2r2nrhtfbflphpnxqkp56jga

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.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math9222880 fatcat:svmv3ppkifgg3eqfohxp3h462q

Android Malware Classification Using K-Means Clustering Algorithm

Isredza Rahmi A Hamid, Nur Syafiqah Khalid, Nurul Azma Abdullah, Nurul Hidayah Ab Rahman, Chuah Chai Wen
2017 IOP Conference Series: Materials Science and Engineering  
This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms.  ...  We classify the Android malware into three clusters which are ransomware, scareware and goodware.  ...  Acknowledgement The authors express appreciation to the University Tun Hussein Onn Malaysia (UTHM). This research is supported by Short Term Grant vot number U653 and Gates IT Solution Sdn.  ... 
doi:10.1088/1757-899x/226/1/012105 fatcat:jptdos7nyjfgto5kkgqqhmyx6y

Identifying Unknown Android Malware with Feature Extractions and Classification Techniques

Ludovic Apvrille, Axelle Apvrille
2015 2015 IEEE Trustcom/BigDataSE/ISPA  
To demonstrate the efficiency of our approach, we have extracted properties and classified over 600,000 applications during two crawling campaigns in July 2014 and October 2014, with the detection of one  ...  Alligator is a classification tool that efficiently and automatically combines several classification algorithms.  ...  Feature relevance Based on our experience on Android malware reverse engineering, we identify the mechanisms used to conduct malicious deeds, and extract features around it.  ... 
doi:10.1109/trustcom.2015.373 dblp:conf/trustcom/ApvrilleA15 fatcat:rbhomozwkjg4hm2qsgobrx3fxe

Android Malware Detection Using Parallel Machine Learning Classifiers

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
2014 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies  
This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware.  ...  Hence, incentivizing a new wave of emerging Android malware sophisticated enough to evade most common detection methods.  ...  ACKNOWLEDGMENT We gratefully acknowledge McAfee's support in providing the repository of malware and benign apps used for this research work.  ... 
doi:10.1109/ngmast.2014.23 dblp:conf/ngmast/YerimaSM14 fatcat:sktqormrs5fxndfmzxmoo2iyoa
« Previous Showing results 1 — 15 out of 1,443 results