This dataset provided by Microsoft contains about 9 classes of malware. ,
Source: https://www.kaggle.com/c/malware-classification
Minimize multi-class error. Multi-class probability estimates. Malware detection should not take hours and block the user's computer. It should fininsh in a few seconds or a minute. 2. Machine Learning Problem 2.1. Data 2.1.1. Data Overview Source : https://www.kaggle.com/c/malware-classification/data For every malware, we have two files .asm file (read more: https://www.reviversoft.com/file-extensions/asm) .bytes file (the raw data contains the hexadecimal representation of the file's binary content, without the PE header) Total train dataset consist of 200GB data out of which 50Gb of data is .bytes files and 150GB of data is .asm files: Lots of Data for a single-box/computer. There are total 10,868 .bytes files and 10,868 asm files total 21,736 files There are 9 types of malwares (9 classes) in our give data Types of Malware: Ramnit Lollipop Kelihos_ver3 Vundo Simda Tracur Kelihos_ver1 Obfuscator.ACY Gatak There are nine different classes of malware that we need to classify a given a data point => Multi class classification problem Source: https://www.kaggle.com/c/malware-classification#evaluation Multi class log-loss Confusion matrix Objective: Predict the probability of each data-point belonging to each of the nine classes.