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dc.contributor.authorLe, Tran Duc-
dc.contributor.authorLe, Ba Luong-
dc.contributor.authorDinh, Truong Duy-
dc.contributor.authorPham, Van Dai-
dc.descriptionLecture Notes in Networks and Systems (LNNS, volume 734); CITA: Conference on Information Technology and its Applications; pp: 37-49.vi_VN
dc.description.abstractThe primary objective of this research is to propose a novel method for analyzing malware through the utilization of hashing techniques. The proposed approach integrates the use of Import Hash, Fuzzy Hash, and Section Level Fuzzy Hash (SLFH) to create a highly optimized, efficient, and accurate technique to classify ransomware families. To test the proposed methodology, we collected a comprehensive dataset from reputable sources and manually labelled each sample to augment the reliability and precision of our analysis. During the development of the proposed methodology, we introduced new steps and conditions to identify ransomware families, resulting in the highest performance level. The major contributions of this research include the combination of various hashing techniques and the proposal of a hash comparison strategy that facilitates the comparison of section hashes between ransomware and the pre-build database.vi_VN
dc.publisherSpringer Naturevi_VN
dc.subjectImport Hashvi_VN
dc.subjectFuzzy Hashvi_VN
dc.subjectFile Level Section Hashingvi_VN
dc.titleClassification of Ransomware Families Based on Hashing Techniquesvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:CITA 2023 (International)

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