Project Adleman, is a machine learning project that aims to explore techniques to classify Malware effectively. The learnings form this project were used to compete in the Microsoft Malware Challange (BIG 2015) hosted by Kaggle.
One of the major challenges that anti-malware faces today is the vast amount of data and files which need to be evaluated for potential malicious intent. This generates tens of millions of daily data points to be analyzed. 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.
For this challange, Microsoft has provided about 500 MB of data
Notable links (provided by Kaggle):
- [Detailed problem description] (https://www.kaggle.com/c/malware-classification)
- Evaluation criteria
- Competition Rules
- Prizes
- Timelines
Refrences:
- Cover Images
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