A Stacking Ensemble Framework for Android Malware Prediction

Author:

Bhattacharya Abhishek,Dutta Soumi,Krit Salahddine,Lai Wen Cheng,Azzaoui Nadjet,Burlea-Schiopoiu Adriana

Publisher

Springer Nature Singapore

Reference14 articles.

1. Bhattacharya A, Goswami RT (2017) DMDAM: data mining based detection of Android malware. In: Mandal J, Satapathy S, Sanyal M, Bhateja V (eds) Proceedings of the first international conference on intelligent computing and communication. Advances in intelligent systems and computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_20

2. Bhattacharya A, Goswami RT (2017) Comparative analysis of different feature ranking techniques in data mining-based Android malware detection. In: Satapathy S, Bhateja V, Udgata S, Pattnaik P (eds) Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Advances in intelligent systems and computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_5

3. Bhattacharya A, Goswami RT (2018) A hybrid community based rough set feature selection technique in Android malware detection. In: Yang XS, Nagar A, Joshi A (eds) Smart trends in systems, security and sustainability. Lecture notes in networks and systems, vol 18. Springer, Singapore. https://doi.org/10.1007/978-981-10-6916-1_23

4. Bhattacharya A, Goswami RT, Mukherjee K (2019) A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. Int J Mach Learn Cyber 10:1893–1907

5. Neumann U, Genze N, Heider D (2017) EFS: an ensemble feature selection tool implemented as R-package and web-application. BioData Min 10(1):21. https://doi.org/10.1186/s13040-017-0142-8

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1. A New Android Malware Detection Approach Using Multilayer Stacking;2024 10th International Conference on Web Research (ICWR);2024-04-24

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