DMDAM: Data Mining Based Detection of Android Malware-Reference-Cited by-同舟云学术

DMDAM: Data Mining Based Detection of Android Malware

Published:2016-11-23 Issue: Volume: Page:187-194
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Abhishek Bhattacharya ,Goswami Radha Tamal

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-2035-3_20

Reference13 articles.

1. Borja Sanz, Igor Santos, Xabier Ugarte-Pedrero, Carlos Laorden, Javier Nieves, and Pablo Garcia Bringas, “Instance-based Anomaly Method for Android Malware Detection”, SECRYPT, SciTePress, pp. 387–394, 2013.

2. I. Burquera, U. Zurutuza, and S. Nadjm-Tehrani, “Crowdroid: behavior-based malware detection system for Android,” in Proc. the 1st ACM workshop on Security and privacy in smartphones and mobile devices, 2011, pp. 15–26, 2011.

3. K. Allix, T. F. D. A. Bissyande, J. Klein, and Y. Le Traon,, “Machine Learning-Based Malware Detection for Android Applications: History Matters!,” Technical Report, University of Luxembourg, 2014.

4. Wei Xu, Fangfang Zhang, Sencun Zhu, “Permlyzer: Analyzing permission usage in Android applications”, In Proc. of IEEE International Symposium on Software reliability Engineering (ISSRE), pp. 400–410, 2013.

5. Ryo Sato, Daiki Chiba and Shigeki Goto, “Detecting Android Malware by Analyzing Manifest Files”, Proceedings of the APAN – Network Research Workshop, pp. 1–9, 2013.

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive review on permissions-based Android malware detection;International Journal of Information Security;2024-03-04

2. Comparison of Malware Detection Techniques Using Machine Learning Algorithms;Lecture Notes in Networks and Systems;2024

3. Analysis of Android Malware Detection (AMD)To Recognisecryptoware Usingdeep Learning;2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS);2023-10-27

4. A Comparative Analysis of Performances of Different Ensemble Approaches for Classification of Android Malwares;Emerging Technologies in Data Mining and Information Security;2022-09-29

5. A Stacking Ensemble Framework for Android Malware Prediction;Emerging Technologies in Data Mining and Information Security;2022-09-29