An effective ransomware detection approach in a cloud environment using volatile memory features
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Hardware and Architecture,Software,Computer Science (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s11416-022-00425-2.pdf
Reference29 articles.
1. Alhawi, O.M., Baldwin, J., Dehghantanha, A.: Leveraging machine learning techniques for windows ransomware network traffic detection. In: Cyber threat intelligence, pp. 93–106. Springer, Cham (2018)
2. Andronio, N., Zanero, S., Maggi, F.: Heldroid: Dissecting and detecting mobile ransomware. In: international symposium on recent advances in intrusion detection, pp. 382–404. Springer, Cham. (2015)
3. Barabosch, T., Bergmann, N., Dombeck, A., Padilla, E.: Quincy: Detecting host-based code injection attacks in memory dumps. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 209–229. Springer, Cham (2017)
4. Bhardwaj, A., Avasthi, V., Sastry, H., Subrahmanyam, G.V.B.: Ransomware digital extortion: a rising new age threat. Indian J. Sci. Technol. 9(14), 1–5 (2016)
5. Cabaj, K., Mazurczyk, W.: Using software-defined networking for ransomware mitigation: the case of cryptowall. IEEE Network 30(6), 14–20 (2016)
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