Using Federated Learning on Malware Classification
Kuang-Yao Lin, Weiren Huang
Abstract
In recent years, everything has been more and more systematic, and it would generate many cyber security issues. One of the most important of these is the malware. Modern malware has switched to a high-growth phase. According to the AV-TEST Institute showed that there are over 350,000 new malicious programs (malware) and potentially unwanted applications (PUA) be registered every day. This threat was presented and discussed in the present paper. In addition, we also considered data privacy by using federated learning. Feature extraction can be performed based on malware. The proposed method achieves very high accuracy (≈0.9167) on the dataset provided by VirusTotal.
Topics & Concepts
MalwareComputer scienceComputer securityFeature extractionFeature (linguistics)Machine learningArtificial intelligenceData miningPhilosophyLinguisticsAdvanced Malware Detection TechniquesNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-voting