Detecting Android Malware by Combining System Call Sequence Relationships with Local Feature Calculation

Author:

Hung Chien-Hui,Chen Yi-ming,Wu Chao-Ching

Publisher

Springer Nature Singapore

Reference18 articles.

1. Kaspersky: IT threat evolution Q3 2021. Mobile statistics (2021). https://securelist.com/it-threat-evolution-in-q3-2021-mobile-statistics/105020/

2. StatCounter: Mobile Operating System Market Share Worldwide (2022). https://gs.statcounter.com/os-market-share/mobile/worlwide/#monthly-202101-202203-bar

3. Saracino, A., Sgandurra, D., Dini, G., Martinelli, F.: MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention (2016)

4. Feng, P., Ma, J., Sun, C., Xu, X., Ma, Y.: A novel dynamic android malware detection system with ensemble learning. IEEE Trans. Depend. Secur. Comput. 20, 1–10 (2018)

5. Liu, Y.-C.: A call sequence compression method for local weighting system using mobile malware detection. Master's thesis, Institute of Information Management, National Central University (2021)

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1. Maaker: A framework for detecting and defeating evasion techniques in Android malware;Journal of Information Security and Applications;2023-11

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