Ransomware Taxonomy and Detection Techniques Based on Machine Learning: A Review
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45124-9_11
Reference54 articles.
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3. Kilgallon, S., De La Rosa, L., Cavazos, J.: Improving the effectiveness and efficiency of dynamic malware analysis with machine learning. In: Proceedings - 2017 Resilience Week, RWS 2017 (2017). https://doi.org/10.1109/RWEEK.2017.8088644
4. Babaagba, K.O., Adesanya, S.O.: A study on the effect of feature selection on malware analysis using machine learning. ACM Int. Conf. Proc. Ser. (2019). https://doi.org/10.1145/3318396.3318448
5. Aurangzeb, S., Bin Rais, R.N., Aleem, M., Islam, M.A., Iqbal, M.A.: On the classification of Microsoft-Windows ransomware using hardware profile. PeerJ Comput. Sci. 7, e361 (2021). https://doi.org/10.7717/peerj-cs.361
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