Malware Detection on Server using Distributed Machine Learning

Abstract

Malware has continued to develop at a disturbing rate despite on-going reduction efforts. This has been considerably more pervasive on web servers, where server computing is an increasingly popular platform for both industry and buyers. As of late, another age of malware families has developed with advanced evasion abilities which make them substantially difficult to identify utilizing ordinary techniques. On one hand, the popularity of worldwide use of Internet absorbs attention of most engineers for delivering their applications. The expanded number of applications, on the other hand, prepares an appropriate prone for a few users to create various types of malware and include them in the market or in outsider markets as sheltered applications. This paper proposes and explores a machine learning based characterization approach for identification of malware and utilizes distributed Support Vector Machine (SVM) algorithm keeping in mind the end goal to detect malicious software(malware) in server computing platform using malicious and benign records.