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DAOUDI Nadia

University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX

Main Referenced Co-authors
BISSYANDE, Tegawendé  (7)
KLEIN, Jacques  (6)
ALLIX, Kevin  (5)
SAMHI, Jordan  (2)
Allix, Kevin (1)
Main Referenced Keywords
Android (4); Android Security (3); Deep Learning (3); DREBIN (2); Machine Learning (2);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Trustworthy Software Engineering (TruX) (2)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > TruX - Trustworthy Software Engineering (1)
Main Referenced Disciplines
Computer science (8)

Publications (total 8)

The most downloaded
321 downloads
Sun, T., Daoudi, N., Allix, K., & Bissyande, T. F. D. A. (2021). Android Malware Detection: Looking beyond Dalvik Bytecode. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). doi:10.1109/ASEW52652.2021.00019 https://hdl.handle.net/10993/48892

The most cited

18 citations (Scopus®)

Daoudi, N., Samhi, J., Kabore, A. K., Allix, K., Bissyande, T. F. D. A., & Klein, J. (2021). DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode. In Communications in Computer and Information Science. Springer. doi:10.1007/978-3-030-87839-9_4 https://hdl.handle.net/10993/48789

Daoudi, N. (2023). REVISITING AND BOOSTING STATE-OF-THE-ART ML-BASED ANDROID MALWARE DETECTORS [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/54218

Daoudi, N., Allix, K., Bissyande, T. F. D. A., & Klein, J. (2023). Guided Retraining to Enhance the Detection of Difficult Android Malware. In 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023). doi:10.1145/3597926.3598123
Peer reviewed

Daoudi, N., Allix, K., Bissyande, T. F. D. A., & Klein, J. (December 2022). Assessing the opportunity of combining state-of-the-art Android malware detectors. Empirical Software Engineering, 28. doi:10.1007/s10664-022-10249-9
Peer Reviewed verified by ORBi

Samhi, J., Gao, J., Daoudi, N., Graux, P., Hoyez, H., Sun, X., Allix, K., Bissyande, T. F. D. A., & Klein, J. (2022). JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis. In 44th International Conference on Software Engineering (ICSE 2022). doi:10.1145/3510003.3512766
Peer reviewed

Daoudi, N., Allix, K., Bissyande, T. F. D. A., & Klein, J. (May 2022). A Deep Dive inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores. ACM Transactions on Privacy and Security, 25 (2). doi:10.1145/3503463
Peer reviewed

Sun, T., Daoudi, N., Allix, K., & Bissyande, T. F. D. A. (2021). Android Malware Detection: Looking beyond Dalvik Bytecode. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). doi:10.1109/ASEW52652.2021.00019
Peer reviewed

Daoudi, N., Allix, K., Bissyande, T. F. D. A., & Klein, J. (2021). Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection. Empirical Software Engineering, 26. doi:10.1007/s10664-021-09955-7
Peer Reviewed verified by ORBi

Daoudi, N., Samhi, J., Kabore, A. K., Allix, K., Bissyande, T. F. D. A., & Klein, J. (2021). DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection Based on Image Representation of Bytecode. In Communications in Computer and Information Science. Springer. doi:10.1007/978-3-030-87839-9_4
Peer reviewed

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