  Machine learning is rapidly becoming one of the most important technology for
malware traffic detection, since the continuous evolution of malware requires a
constant adaptation and the ability to generalize. However, network traffic
datasets are usually oversized and contain redundant and irrelevant
information, and this may dramatically increase the computational cost and
decrease the accuracy of most classifiers, with the risk to introduce further
noise.
  We propose two novel dataset optimization strategies which exploit and
combine several state-of-the-art approaches in order to achieve an effective
optimization of the network traffic datasets used to train malware detectors.
The first approach is a feature selection technique based on mutual information
measures and sensibility enhancement. The second is a dimensional reduction
technique based autoencoders. Both these approaches have been experimentally
applied on the MTA-KDD'19 dataset, and the optimized results evaluated and
compared using a Multi Layer Perceptron as machine learning model for malware
detection.
