Classify Malware Into Families
Based on File Content and Characteristics
In recent years, the malware
industry has become a well organized market involving large amounts of money.
Well funded, multi-player syndicates invest heavily in technologies and capabilities
built to evade traditional protection, requiring anti-malware vendors to
develop counter mechanisms for finding and deactivating them. In the meantime,
they inflict real financial and emotional pain to users of computer systems.
One
of the major challenges that anti-malware faces today is the vast amounts of
data and files which need to be evaluated for potential malicious intent. For
example, Microsoft's real-time detection anti-malware products are present on
over 160M computers worldwide and inspect over 700M computers monthly. This
generates tens of millions of daily data points to be analyzed as potential
malware. One of the main reasons for these high volumes of different files is
the fact that, in order to evade detection, malware authors introduce
polymorphism to the malicious components. This means that malicious files
belonging to the same malware "family", with the same forms of
malicious behavior, are constantly modified and/or obfuscated using various
tactics, such that they look like many different files.
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| Malware Classification |
In order to be effective in
analyzing and classifying such large amounts of files, we need to be able to
group them into groups and identify their respective families. In addition,
such grouping criteria may be applied to new files encountered on computers in
order to detect them as malicious and of a certain family.
For this challenge, Microsoft is
providing the data science community with an unprecedented malware dataset and
encouraging open-source progress on effective techniques for grouping variants
of malware files into their respective families.
Acknowledgements
This competition is hosted by WWW2015 / BIG 2015 and the following Microsoft groups: Microsoft MalwareProtection Center, Microsoft Azure Machine Learning and Microsoft Talent
Management.
Started: 6:49 pm, Tuesday 3
February 2015 UTC
Ends: 11:59 pm, Friday 17 April
2015 UTC (73 total days)
Points: this competition awards
standard ranking points


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