  The application of machine learning to bioinformatics problems is well
established. Less well understood is the application of bioinformatics
techniques to machine learning and, in particular, the representation of
non-biological data as biosequences. The aim of this paper is to explore the
effects of giving amino acid representation to problematic machine learning
data and to evaluate the benefits of supplementing traditional machine learning
with bioinformatics tools and techniques. The signatures of 60 computer viruses
and 60 computer worms were converted into amino acid representations and first
multiply aligned separately to identify conserved regions across different
families within each class (virus and worm). This was followed by a second
alignment of all 120 aligned signatures together so that non-conserved regions
were identified prior to input to a number of machine learning techniques.
Differences in length between virus and worm signatures after the first
alignment were resolved by the second alignment. Our first set of experiments
indicates that representing computer malware signatures as amino acid sequences
followed by alignment leads to greater classification and prediction accuracy.
Our second set of experiments indicates that checking the results of data
mining from artificial virus and worm data against known proteins can lead to
generalizations being made from the domain of naturally occurring proteins to
malware signatures. However, further work is needed to determine the advantages
and disadvantages of different representations and sequence alignment methods
for handling problematic machine learning data.
