Dataset Optimization Strategies for MalwareTraffic Detection

TL;DR

This paper introduces two novel dataset optimization strategies, feature selection and autoencoder-based dimensionality reduction, to improve malware traffic detection efficiency and accuracy.

Contribution

It proposes combined feature selection and autoencoder techniques specifically tailored for optimizing network traffic datasets for malware detection.

Findings

Optimized datasets improved detection accuracy.

Reduced computational cost for classifiers.

Effective noise reduction in datasets.

Abstract

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.