PUMA: Permission Usage to Detect Malware in Android-Reference-Cited by-同舟云学术

PUMA: Permission Usage to Detect Malware in Android

Published:2013 Issue: Volume: Page:289-298
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Sanz Borja,Santos Igor,Laorden Carlos,Ugarte-Pedrero Xabier,Bringas Pablo Garcia,Álvarez Gonzalo

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-33018-6_30

Reference15 articles.

1. Schultz, M., Eskin, E., Zadok, F., Stolfo, S.: Data mining methods for detection of new malicious executables. In: Proceedings of the 2001 IEEE Symposium on Security and Privacy, pp. 38–49 (2001)

2. Devesa, J., Santos, I., Cantero, X., Penya, Y.K., Bringas, P.G.: Automatic Behaviour-based Analysis and Classification System for Malware Detection. In: Proceedings of the 12th International Conference on Enterprise Information Systems (ICEIS), pp. 395–399 (2010)

3. Santos, I., Nieves, J., Bringas, P.G.: Semi-supervised learning for unknown malware detection. In: Proceedings of the 4th International Symposium on Distributed Computing and Artificial Intelligence (DCAI), 9th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), pp. 415–422 (2011)

4. Santos, I., Laorden, C., Bringas, P.G.: Collective classification for unknown malware detection. In: Proceedings of the 6th International Conference on Security and Cryptography (SECRYPT), pp. 251–256 (2011)

5. Santos, I., Brezo, F., Ugarte-Pedrero, X., Bringas, P.G.: Opcode sequences as representation of executables for data-mining-based unknown malware detection. Information Sciences (in press), doi:10.1016/j.ins.2011.08.020

Cited by 94 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. Android Malware Detection Using Machine Learning: A Review;Lecture Notes in Networks and Systems;2024

2. Comparing Classifiers: A Look at Machine-Learning and the Detection of Mobile Malware in COVID-19 Android Mobile Applications;Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing;2023-10-16

3. Experimental comparison of features, analyses, and classifiers for Android malware detection;Empirical Software Engineering;2023-09-26

4. A Machine Learning Method for Detecting Behavior-Based Malware;2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA);2023-08-03

5. NMal-Droid: network-based android malware detection system using transfer learning and CNN-BiGRU ensemble;Wireless Networks;2023-06-27