Detect and Classify Zero Day Malware Efficiently In Big Data Platform

Abstract

Malware has long been familiar on the Internet nowadays as one of the most prominent cyber threats. It expands rapidly in volume, velocity and variety, which overcoming the conventional methods used to identify and recognize malware. In order to suit the size and difficulty of such a data-accelerated environment, successful analytics methods are required. Nowadays sense of Big Data platform, the specific methods will help malware researchers successfuldone the time-consuming process of systematically investigating malicious events. Security researchers want to create a use of Machine Learning (ML) algorithms with big data techniques to evaluate and track indefinite malware in a large scale. These techniques consists of dynamic and wide flux of malicious binaries which aid them to solve the emerging threat environment. This paper suggests the framework of big data whereby techniques of static and dynamic malware detection are efficiently merged in order to accurately classify and identify zero-day malware. The framework being introduced the tested and estimated on a sample files for 0.1 million involving the clean files for 0.03 million and containing a wide variety of malware families in 0.13 million malicious binaries. The results show that SVM attained the best accuracy of 93.03% for detecting malware and benign types using 10-fold cross validation.