A Ransomware Classification Framework Based on File-Deletion and File-Encryption Attack Structures
Authors: Aaron Zimba, Mumbi Chishimba, Sipiwe Chihana
Abstract: Ransomware has emerged as an infamous malware that has not escaped a lot of myths and inaccuracies from media hype. Victims are not sure whether or not to pay a ransom demand without fully understanding the lurking consequences. In this paper, we present a ransomware classification framework based on file-deletion and file-encryption attack structures that provides a deeper comprehension of potential flaws and inadequacies exhibited in ransomware. We formulate a threat and attack model representative of a typical ransomware attack process from which we derive the ransomware categorization framework based on a proposed classification algorithm. The framework classifies the virulence of a ransomware attack to entail the overall effectiveness of potential ways of recovering the attacked data without paying the ransom demand as well as the technical prowess of the underlying attack structures. Results of the categorization, in increasing severity from CAT1 through to CAT5, show that many ransomwares exhibit flaws in their implementation of encryption and deletion attack structures which make data recovery possible without paying the ransom. The most severe categories CAT4 and CAT5 are better mitigated by exploiting encryption essentials while CAT3 can be effectively mitigated via reverse engineering. CAT1 and CAT2 are not common and are easily mitigated without any decryption essentials.
Explore the paper tree
Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant
Look for similar papers (in beta version)
By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.