Abstract
In classifying malware, an open research question is how to combine similar extracted data from program analyzers in such a way that the advantages of the analyzers accrue and the errors are minimized. We propose an approach to fusing multiple program analysis outputs by abstracting the features to a common form and utilizing a disjoint union fusion function. The approach is evaluated in an experiment measuring classification accuracy on fused dynamic trace data on over 18,000 malware files. The results indicate that a naïve fusion approach can yield improvements over non-fused results, but the disjoint union fusion function outperforms naïve union by a statistically significant amount in three of four classification methods applied.
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LeDoux, C., Walenstein, A., Lakhotia, A. (2012). Improved Malware Classification through Sensor Fusion Using Disjoint Union. In: Dua, S., Gangopadhyay, A., Thulasiraman, P., Straccia, U., Shepherd, M., Stein, B. (eds) Information Systems, Technology and Management. ICISTM 2012. Communications in Computer and Information Science, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29166-1_32
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DOI: https://doi.org/10.1007/978-3-642-29166-1_32
Publisher Name: Springer, Berlin, Heidelberg
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