DNS exfiltration detection in the presence of adversarial attacks and modified exfiltrator behaviour

The Domain Name System (DNS) exfiltration is an activity in which an infected device sends data to the attacker’s server by encoding it in DNS request messages. Because of the frequent use of DNS exfiltration for malicious purposes, exfiltration detection gained attention from the research community which proposed several predominantly machine learning-based methods. The majority of previous studies used publicly available DNS exfiltration tools with the default configuration parameters, resulting in datasets created from DNS exfiltration requests that are usually significantly longer, have more DNS name labels, and higher character entropy than average regular DNS requests. This further led to overly optimistic detection rates. In this paper, we have explored some of the strategies an attacker could use to avoid exfiltration detection. First, we have explored the impact of DNS exfiltration tools’ parameter variation on the exfiltration detection accuracy. Second, we have modified the DNSExfiltrator tool to produce exfiltration requests which have significantly lower character entropy. This approach proved to be capable of deceiving classifiers based on single DNS request features. Only around 1% of modified DNS requests shorter or equal to 9 bytes, and less than one third of DNS exfiltration requests in the overall population were accurately detected. In addition, we present a methodology and an aggregated feature set (including inter-request timing statistics) which can be used for accurate DNS exfiltration in this kind of adversarial settings.