SFCGDroid: android malware detection based on sensitive function call graph-Reference-Cited by-同舟云学术

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SFCGDroid: android malware detection based on sensitive function call graph

Published:2023-05-01 Issue:5 Volume:22 Page:1115-1124
ISSN:1615-5262
Container-title:International Journal of Information Security
language:en
Short-container-title:Int. J. Inf. Secur.

Author:

Shi Sibo,Tian Shengwei,Wang Bo,Zhou Tiejun,Chen Guanxin

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10207-023-00679-x.pdf

Reference50 articles.

1. Kaspersky’sreport. https://securelist.com/mobile-malware-evolution-2021/105876. Accessed 21 Feb 2022

2. Zheng, M., Sun, M., Lui, J.C.: Droid analytics: a signature based analytic system to collect, extract, analyze and associate android malware. In: 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications pp. 163–171. IEEE (2013)

3. Saracino, A., Sgandurra, D., Dini, G., Martinelli, F.: Madam: effective and efficient behavior-based android malware detection and prevention. IEEE Trans. Dependable Secur. Comput. 15(1), 83–97 (2016)

4. Zhang, H., Luo, S., Zhang, Y., Pan, L.: An efficient Android malware detection system based on method-level behavioral semantic analysis. IEEE Access 7, 69246–69256 (2019)

5. Aafer, Y., Du, W., Yin, H.: Droidapiminer: mining api-level features for robust malware detection in android. In: International Conference on Security and Privacy in Communication Systems, pp. 86-103. Springer, Cham (2013)

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2. Comparative Analysis of Machine Learning Models for Android Malware Detection;Sakarya University Journal of Science;2024-06-30

3. Android Malware Detection based on Feature-pair Bonding: A Hybrid Detection Model;2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N);2023-12-15

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