A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments
Author:
Affiliation:
1. Huazhong University of Science and Technology, Wuhan, China
2. Nanyang Technological University, Singapore, Singapore
Funder
Fundamental Research Funds for the Central Universities, HUST,
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3597503.3623320
Reference63 articles.
1. 2023. https://github.com/Maruko912/AMDs.
2. 2023. Allatori. https://allatori.com/.
3. Adversarial Deep Learning for Robust Detection of Binary Encoded Malware
4. AndroZoo
5. Obfuscapk: An open-source black-box obfuscation tool for Android apps
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4. Enhancing Malware Classification via Self-Similarity Techniques;IEEE Transactions on Information Forensics and Security;2024
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