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- W4224581290 abstract "Cloud computing has become one of the most preferred solutions for enterprises to implement and extend various enterprise applications. The importance of virtual servers in cloud computing makes them a lucrative target among attackers. Current security mechanisms can be circumvented by malware present on same machine. This paper presents an approach for reliable ransomware detection on an enterprise’s private cloud. It captures the volatile memory state of virtual machines and extracts a valuable set of RAM, file system and network features after execution of benign and malicious samples. Further, feature selection and machine learning techniques are applied to these extracted features for determining the effectiveness of proposed set of features. The proposed methodology is evaluated in four extensive experiments and results depict that it can differentiate between benign and ransomware samples. Random Forest classifier performed best in all experiment setups in comparison to all other classifiers. The proposed methodology can effectively serve as a basis for detecting infection in enterprise virtual machines." @default.
- W4224581290 created "2022-04-27" @default.
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- W4224581290 date "2022-04-25" @default.
- W4224581290 modified "2023-09-27" @default.
- W4224581290 title "An effective ransomware detection approach in a cloud environment using volatile memory features" @default.
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- W4224581290 doi "https://doi.org/10.1007/s11416-022-00425-2" @default.
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