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DAOUDI Nadia

Main Referenced Co-authors
BISSYANDE, Tegawendé  (9)
KLEIN, Jacques  (9)
ALLIX, Kevin  (8)
SUN, Tiezhu  (4)
KIM, Kisub  (2)
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 (11)

Publications (total 11)

The most downloaded
500 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

43 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

SUN, T., DAOUDI, N., PIAN, W., KIM, K., ALLIX, K., BISSYANDE, T., & KLEIN, J. (2024). Temporal-Incremental Learning for Android Malware Detection. ACM Transactions on Software Engineering and Methodology. doi:10.1145/3702990
Peer Reviewed verified by ORBi

SUN, T., DAOUDI, N., KIM, K., ALLIX, K., BISSYANDE, T., & KLEIN, J. (2024). DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware. In DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware. New York City, United States: Association for Computing Machinery (ACM). doi:10.1145/3674805.3690745
Peer reviewed

SUN, T., PIAN, W., DAOUDI, N., ALLIX, K., F. Bissyandé, T., & KLEIN, J. (2024). LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning. In A. Rapp & L. Di Caro (Eds.), Natural Language Processing and Information Systems - 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-031-70239-6_5
Peer reviewed

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 verified by ORBi

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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