Author:
Appice Annalisa,Andresini Giuseppina,Malerba Donato
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software
Reference59 articles.
1. Alam, M. S., & Vuong, S. T. (2013). Random forest classification for detecting android malware. In Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 663–669.
2. Alzaylaee, M., Yerima, S., & Sezer, S. (2017). Improving dynamic analysis of android apps using hybrid test input generation. In International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2017): Proceedings, pp. 1–8. IEEE, DOI https://doi.org/10.1109/CyberSecPODS.2017.8074845, (to appear in print).
3. Alzaylaee, M. K., Yerima, S. Y., & Sezer, S. (2020). Dl-droid: Deep learning based android malware detection using real devices. Computers & Security, 89(101), 663. https://doi.org/10.1016/j.cose.2019.101663.
4. Andresini, G., Appice, A., & Malerba, D. (2020). Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification, pp. 173–187. Springer International Publishing: Cham, Switzerland.
5. Appice, A., Guccione, P., & Malerba, D. (2017). A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data. Pattern Recognition, 63, 229–245.
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