A Feature Selection Approach Towards the Standardization of Network Security Datasets

The increasing reliance on telematic applications has made networks attractive targets for cybercriminals. To address this concern, securing sensitive data by including a Network Intrusion Detection System (NIDS) is effective and feasible. Several approaches based on machine learning have been proposed to improve the detection accuracy of these systems and overcome associated drawbacks. However, machine learning is poorly adopted in the network domain, mainly because most datasets are outdated and guidance on selecting features that significantly contribute to a more efficient and effective learning process is needed. In this light, this paper proposes a standard set of features by applying feature selection techniques. A search on the feature space is conducted on five datasets to determine the best subset per dataset. The commonalities among the best subsets are then identified to define a standard set. Validation shows that, beyond improving the learning process, feature selection provides encouraging results for standardization of network datasets.