Fusing Heterogeneous Data for Network Asset Classification – A Two-layer Approach

An essential aspect of cybersecurity management is maintaining knowledge of the assets in the protected network. Automated asset discovery and classification can be done using various methods, differing in reliability and the provided type of information. Therefore, deploying multiple methods and combining their results is usually needed – but this is a nontrivial task. In this paper, we describe our case of how we got to the need for such a data fusion method, how we approached it, and we present our solution – a two-layer data fusion method that can effectively fuse multiple heterogeneous and unreliable sources of information about a network device to classify it. The method is based on a combination of expert-written conditions, machine learning from small amounts of data, and the Dempster-Shafer theory of evidence. We evaluate the method on the task of operating system recognition using data from real network traffic and several generated datasets simulating different conditions.