CloudIntell investigates how features extracted from executable files can support malware detection using machine learning.
The research question
How can an analysis pipeline combine executable features and machine-learning methods to distinguish malicious files from benign ones? CloudIntell examines this question in a cloud-based detection framework.
The approach
The paper brings feature extraction and classification together in a malware detection system. Read the published work for its experimental design, dataset, evaluation and results.
This page is a short portfolio summary of the published paper. It does not reproduce the paper’s evaluation or imply a current production service.
Read the publication
Qublai Khan Ali Mirza, Irfan Awan and Muhammad Younas. CloudIntell: An intelligent malware detection system. Future Generation Computer Systems, Volume 86, 2018, pages 1042–1053.