Malware Analysis Using Classification and Clustering Algorithms

Abstract

Malware analysis and detection are important tasks to be accomplished as malware is getting more and more arduous at every instance. The threats and problems posed by the public around the globe are also rapidly increasing. Detection of zero-day attacks and polymorphic viruses is also a challenging task to be done. The increasing threats and problems lead to the need for detection techniques which lead to the well-known and the most common approach called as machine learning. The purpose of this survey is to formulate the most effective feature extraction and classification ways that sums up the most effective methods (which includes algorithms) with maximum accuracy and also to effectively understand the clustering properties of the malware datasets by considering appropriate algorithms. This work also provides an overview on information about malwares used. The experimental results of the proposed model clearly showed that the KNN classifier as the most accurate with 0.962355 accuracy.