Fast Model Learning for the Detection of Malicious Digital Documents

Daniel Scofield, Craig Miles & Stephen Kuhn
Modern cyber attacks are often conducted by distributing digital documents that contain malware. The approach detailed herein, which consists of a classifier that uses features derived from dynamic analysis of a document viewer as it renders the document in question, is capable of classifying the disposition of digital documents with greater than 98 accuracy even when its model is trained on just small amounts of data. To keep the classification model itself small and thereby...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.