Study of Detecting Computer Viruses in Real-Infected Files in the n-Gram Representation with Machine Learning Methods

Machine learning methods wet e successfully applied in recent, years for detecting new and unseen computer viruses The viruses were, however; detected in small virus loader files and not, in reed infected executable files We created data sets of benign files, virus loader files and real infected executable files and represented the data as collections of n-grams Our results indicate that, detecting viruses in real infected executable files with machine, learning methods is nearly impossible in the representation This statement is underpinned by exploring the n-gram representation front an information theoretic: perspective and empirically by ming classification experiments with machine learning methods