Eigenviruses for metamorphic virus recognition

Abstract

Metamorphic virus recognition is the most challenging task for antivirus software, because such viruses are the hardest to detect as they change their appearance and structure on each new infection. In this study, the authors present an effective system for metamorphic virus recognition based on statistical machine learning techniques. The authors approach has successfully scored high detection rate for tested metamorphic virus classes and very low false-positive errors. The system is also able to learn new patterns of viruses for future recognition. The authors conclude the results of their simulation with results analysis and future enhancements in the system to detect other virus classes.