Malware Classification using Machine Learning

If you love to explore large and challenging data sets, then probably you should give Microsoft Malware Classification a try. Before diving deep in to the problem let's take few points on what can you expect to learn from this: In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust software to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware. We can map the business problem to a multi-class classification problem, where we need to predict the class for each given byte files among nine categories (Ramnit, Lollipop, Kelihos_ver3, Vundo, Simda,Tracur, Kelihos_ver1, Obfuscator.ACY, Gatak). Constrains: We need to provide the class probability, wrongly classified class labels should be penalized(that's why log loss has been chosen as KPI) and there should some latency bound.