Scientific Classification of Malware from Practice and Multi-Label Mechanism for Risky Behaviors
Year: 2024
Type: article
Abstract: Objective: In order to respond to the demand of academia and industry for scientific malware classification methods Method: based on the existing work, this study draws on the advantages of Kaspersky's relatively rigorous multi-segment classification and naming, and is carried out according to the idea of emphasizing mutual exclusivity, complete coverage, and convergence, and is combined with the threat risk behavior labels. Results: A set of malware classification framework that conforms to MECE principles, converges classification, and is compatible with industrial fact classification has been formed. Implication: It can effectively support security defense and governance.
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403437017