2 code implementations • 15 May 2019 • Felipe N. Ducau, Ethan M. Rudd, Tad M. Heppner, Alex Long, Konstantin Berlin
With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.
1 code implementation • 13 Mar 2019 • Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard Harang
In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss.