Investigation and pre-processing of CLaMP mlaware dataset for machine learning models

The availability of a standard and a suitable dataset is a primary requirement of machine learning (ML) systems. The ML classification is highly effective in mal ware detection and classification but still it faces challenges in data preprocessing. This research study has investigated and preprocessed the popular CLaMP malware dataset for ML classifiers. The raw CLaMP dataset have been utilized for training and testing the SVM and k-NN machine learning (ML) classifiers. The investigation is performed for different kernels and neighbour count of SVM and k-NN classifiers, respectively. CLaMP is a popular malware dataset and which can be further improved by data pre-processing. The data pre-processing as transformation, outlier removal and filling, and data point smoothing further improve the suitability of the CLaMP dataset.