Improved Intelligent Malware Detection Model in Cloud Environment
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8111-3_30
Reference20 articles.
1. El Kafhali, S., El Mir, I., Hanini, M.: Security threats, defense mechanisms, challenges, and future directions in Cloud computing. Arch. Comput. Methods Eng. 29(1), 223–246 (2021). Available at: https://doi.org/10.1007/s11831-021-09573-y
2. Kimmell, J.C., Abdelsalam, M., Gupta, M.: Analyzing ML approaches for online malware detection in cloud. In: 2021 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 189–196. Irvine, CA, USA (2021). https://doi.org/10.1109/SMARTCOMP52413.2021.00046.
3. Kanimozhi, V., Jacob, T.P.: Artificial Intelligence outflanks all other ML classifiers in network intrusion detection system on the realistic cyber dataset CSE-CIC-IDS2018 using Cloud computing. ICT Express 7(3), 366–370 (2021). Available at: https://doi.org/10.1016/j.icte.2020.12.004
4. Karthick, G., Mapp, G., Kammueller, F., Aiash, M.: Modeling and verifying a resource allocation algorithm for secure service migration for commercial Cloud systems. Comput. Intell. 38(3), 811–828 (2022). https://doi.org/10.1111/coin.12421
5. Ramirez, J., Ezenwigbo, O.A., Karthick, G., Trestian, R., Mapp, G.: A new service management framework for vehicular networks. In: 2020 23rd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), pp. 162–164. Paris, France (2020). https://doi.org/10.1109/ICIN48450.2020.9059441.
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