Entropy-based Prediction of Network Protocols in the Forensic Analysis of DNS Tunnels
This addresses the slow and effort-intensive forensic analysis for network security professionals dealing with DNS tunneling.
The paper tackled the problem of detecting malicious DNS tunneling by automating the inference of protocols tunneled within DNS, achieving a prediction accuracy of 75% on a dataset.
DNS tunneling techniques are often used for malicious purposes but network security mechanisms have struggled to detect these. Network forensic analysis has thus been used but has proved slow and effort intensive as Network Forensics Analysis Tools struggle to deal with undocumented or new network tunneling techniques. In this paper we present a method to aid forensic analysis through automating the inference of protocols tunneled within DNS tunneling techniques. We analyze the internal packet structure of DNS tunneling techniques and characterize the information entropy of different network protocols and their DNS tunneled equivalents. From this, we present our protocol prediction method that uses entropy distribution averaging. Finally we apply our method on a dataset to measure its performance and show that it has a prediction accuracy of 75%. Our method also preserves privacy as it does not parse the actual tunneled content, rather it only calculates the information entropy.