Automatic Malware Detection using Data Mining Techniques Based on Power Spectral Density (PSD)

A malware is a software that furtively achieves its process below the appearance of an honest software. Classic approaches apply signatures to separate these software's denote tiny risk to new and hidden instances whose signatures are not offered. The emphasis of malware investigation is instable from applying signature designs to categorising the hateful conduct showed by these malwares. Numerous data mining methods proposed to notice malware mechanically in effectual face. In this thesis, PSD applied to extract the landscapes of malware database and the yield of PSD confidential applying several of data mining methods: Support Vector Machine (SVM), Radial Basis Network (RBF) and multi-layer perceptron (MLP). These techniques presented remarkable results when compared with common researches in this field.