Big data: deep learning for detecting malware

Abstract: 
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 realtime 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.
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Date of publication: 
2018
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Pagination: 
20-26
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Other Papers, Posters and Presentations
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Project sponsor: 
Mobility to Enhance Training of Engineering Graduates in Africa (METEGA); Regional Universities Forum for Capacity Building in Agriculture (RUFORUM)
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