Feature Selection-Based Machine Learning Model for Malware Detection
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47942-7_43
Reference21 articles.
1. Kouliaridis, V., Kambourakis, G.: A comprehensive survey on machine learning techniques for android malware detection. Information (Switzerland). 12(5) (2021). https://doi.org/10.3390/info12050185
2. Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., Liu, H.: A review of android malware detection approaches based on machine learning. IEEE Access. 8, 124579–124607 (2020). https://doi.org/10.1109/ACCESS.2020.3006143
3. Mahindru, A., Sangal, A.L.: SemiDroid: a behavioral malware detector based on unsupervised machine learning techniques using feature selection approaches. Int. J. Mach. Learn. Cybern. 12(5), 1369–1411 (2021). https://doi.org/10.1007/s13042-020-01238-9
4. Pastor, A., et al.: Detection of encrypted cryptomining malware connections with machine and deep learning. IEEE Access. 8, 158036–158055 (2020). https://doi.org/10.1109/ACCESS.2020.3019658
5. Dener, M., Ok, G., Orman, A.: Malware detection using memory analysis data in big data environment. Appl. Sci. (Switzerland). 12(17) (2022). https://doi.org/10.3390/app12178604
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