2023
DOI: 10.1002/cpe.7621
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Robust malware clustering of windows portable executables using ensemble latent representation and distribution modeling

Abstract: Summary Malware is a malicious program used for unauthorized access to organizational infrastructure and systems. To overcome challenges of exponential growth of malware, notable research has been made for unsupervised clustering of Windows‐based portable executable (PE). Nevertheless, to the best of our knowledge there has been no research for robust cluster prediction of Windows based PEs using static features. To this end, we proposed an ensemble neural network architecture for unsupervised feature learning… Show more

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