Singular value decomposition and metamorphic detection

摘要: Metamorphic malware changes its internal structure with each infection, while maintaining original functionality. Such can be difficult to detect, particularly using static analysis, since there may no common signature across infections. In this paper, we apply a score based on Singular Value Decomposition (SVD) the challenging problem of metamorphic detection. SVD, which viewed as specific implementation Principal Component Analysis, is linear algebraic technique that applicable wide range problems where eigenvector analysis useful. Previous research has shown an eigenvector-based derived from facial recognition yields good results when applied reconsider these previous in context and outline strategy defeat such detection scheme.