  Successful malware attacks on information technology systems can cause
millions of dollars in damage, the exposure of sensitive and private
information, and the irreversible destruction of data. Anti-virus systems that
analyze a file's contents use a combination of static and dynamic analysis to
detect and remove/remediate such malware. However, examining a file's entire
contents is not always possible in practice, as the volume and velocity of
incoming data may be too high, or access to the underlying file contents may be
restricted or unavailable. If it were possible to obtain estimates of a file's
relative likelihood of being malicious without looking at the file contents, we
could better prioritize file processing order and aid analysts in situations
where a file is unavailable. In this work, we demonstrate that file names can
contain information predictive of the presence of malware in a file. In
particular, we show the effectiveness of a character-level convolutional neural
network at predicting malware status using file names on Endgame's EMBER
malware detection benchmark dataset.
