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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.\n\nMicrosoft has been very active in building anti-malware products over the years and it runs it’s anti-malware utilities over 150 million computers around the world. This generates tens of millions of daily data points to be analyzed as potential malware. In order to be effective in analyzing and classifying such large amounts of data, we need to be able to group them into groups and identify their respective families.\n\nProblem Statement\nThe dataset provided by Microsoft contains about 9 classes of malware. \nThe problem statement is to build a robust multi class classification model that can accurately classify which class a malware belongs to.\n\nBusiness Objectives and Constraints\n\nMinimize multi-class error.\nMulti-class probability estimates.\nMalware detection should not take hours and block the user's computer. It should fininsh in a few seconds or a minute.\n\nData Overview\n\nFor every malware, we have two files\n\n.asm file (read more: https://www.reviversoft.com/file-extensions/asm)\n\n.bytes file (the raw data contains the hexadecimal representation of the file's binary content, without the PE header)\n\nTotal train dataset consist of 200GB data out of which 50Gb of data is .bytes files and 150GB of data is .asm files:\n\nLots of Data for a single-box/computer.\nThere are total 10,868 .bytes files and 10,868 asm files total 21,736 files\n\nThere are 9 types of malwares (9 classes) in our give data\nTypes of Malware:\nRamnit\nLollipop\nKelihos_ver3\nVundo\nSimda\nTracur\nKelihos_ver1\nObfuscator.ACY\nGatak\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsukanyabag%2Fmicrosoft-malware-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsukanyabag%2Fmicrosoft-malware-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsukanyabag%2Fmicrosoft-malware-detection/lists"}