Android Application Behavior Monitor by Using Hooking Techniques
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50327-6_34
Reference14 articles.
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2. Zhang, J., Tian, C., Duan, Z.: FastDroid: efficient taint analysis for android applications. İn: 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), pp. 236–237 (2019)
3. Arzt, S., Rasthofer, S., Fritz, C., Bodden, E., Bartel, A., Klein, J., et al.: FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps. In: Presented at the Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation, Edinburgh, United Kingdom (2014)
4. Samhi, J., Kober, M., Kabore, A.K., Arzt, S., Bissyandé, T.F., Klein, J.: Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection (2023). arXiv preprint arXiv:2301.03207
5. Rasthofer, S., Arzt, S., Bodden, E.: Machine-Learning Approach for Classifying and Categorizing Android Sources and Sinks (2014)
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