Name
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 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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3195528.3195533 | SEiA |
Keywords | Field | DocType |
Big data Analytics, Malware detection, Machine learning, Deep learning | Data modeling,Computer science,Real-time computing,Feature extraction,Artificial intelligence,Deep learning,Malware,Big data,Machine learning,Scalability | Conference |
ISBN | Citations | PageRank |
978-1-4503-5719-7 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmanuel Masabo | 1 | 6 | 1.64 |
| Kyanda Swaib Kaawaase | 2 | 0 | 0.68 |
| Julianne Sansa-Otim | 3 | 0 | 1.69 |