A Decision Making Model for Collaborative Malware Detection Networks

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Provided by: University of Washington School of Public Health & Community Medicine
Topic: Security
Format: PDF
The increased sophistication and evasiveness of malware has brought tremendous challenges to vendors of antivirus systems. Various malware detection approaches have been proposed and deployed to detect and remove malware. However, it is challenging for a single security vendor to analyze all malware and to provide up-to-date protection, e.g., a signature database. In this paper, the authors investigate the effectiveness of collaboration amongst various antivirus systems and propose a distributed Collaborative Malware Detection Network (CMDN).
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