  With the growing number of malware and cyber attacks, there is a need for
"orthogonal" cyber defense approaches, which are complementary to existing
methods by detecting unique malware samples that are not predicted by other
methods. In this paper, we propose a novel and orthogonal malware detection
(OMD) approach to identify malware using a combination of audio descriptors,
image similarity descriptors and other static/statistical features. First, we
show how audio descriptors are effective in classifying malware families when
the malware binaries are represented as audio signals. Then, we show that the
predictions made on the audio descriptors are orthogonal to the predictions
made on image similarity descriptors and other static features. Further, we
develop a framework for error analysis and a metric to quantify how orthogonal
a new feature set (or type) is with respect to other feature sets. This allows
us to add new features and detection methods to our overall framework.
Experimental results on malware datasets show that our approach provides a
robust framework for orthogonal malware detection.
