An Android Malware Detection Method Based on Deep AutoEncoder-Reference-Cited by-同舟云学术

An Android Malware Detection Method Based on Deep AutoEncoder

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference on ZZZ - AICCC '18
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Author:

He Nengqiang¹,Wang Tianqi²,Chen Pingyang¹,Yan Hanbing¹,Jin Zhengping²

Affiliation:

1. National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC), Beijing, China

2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM Press

Reference16 articles.

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2. Santos I, Brezo F, Ugarte X, et al. 2013. Opcode sequences as representation of executable for data-mining-based unknown malware detection [J]. Information Sciences, 2013, 231(1): 64--82.

3. Iker B, Urko Z, Nadjm-Tehrani S. 2011. Crowdroid: behavior-based malware detection system for Android [C] // Proceedings of the 1st ACM Workshop on Security and Privacy in SmartPhones and Mobile Devices, New York: ACM, 2011: 15--26.

4. Gascon H, Yamaguchi F, Arp D, et al. 2013. Structural detection of Android malware using embedded call graphs [C] // Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security, ACM, 2013: 45--54.

5. Yerima S Y, Sezer S, Muttik I. 2014. Android malware detection using parallel machine learning classifiers [C] // Proceedings of the 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, IEEE, 2014: 37--42.

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