Big data

This is the authors' abstract. We don't add key points for this paper.

Malicious software, commonly known as malware are constantly getting smarter with the capabilities of undergoing self-modifications. They are produced in big numbers and widely deployed very fast through the Internet-capable devices. This is therefore a big data problem and remains challenging in the research community. Existing detection methods should be enhanced in order to effectively deal with today's malware. In this paper, we propose a novel real-time monitoring, analysis and detection approach that is achieved by applying big data analytics and machine learning in the development of a general detection model. The learnings achieved through big data render machine learning more efficient. Using the deep learning approach, we designed and developed a scalable detection model that brings improvement to the existing solutions. Our experiments achieved an accuracy of 97% and ROC of 0.99.