Malware Classification using Bigram BOW, Pixel Intensity Features, and Multiprocessing

The malware classification task launched by Microsoft has been quite popular for the last half a decade. After detecting malware, classification plays an important role, because based on the type of malware, the corresponding action needs to be taken. Feature extraction plays a vital role to proceed ahead with the modeling. The data is in form of two separate files for each malware in consideration, from which we generate the features, choose the top important ones, and train a classical ensemble learning model. There have been various solutions proposed for this task earlier, over which we have made some modifications to achieve better accuracy. We have used the features generated using the bigram Bag of Words (BOW) and included pixel intensity features to approach this task. Since the dataset is quite huge, in this paper we proposed an approach based on multithreading, where instead of processing the data serially, we processed it parallelly through all the cores available in the CPU and optimize the computation time as much as possible.