DNS covert channel detection method using the LSTM model

DNS is a kind of basic network protocol that is rarely blocked by firewalls; therefore, it is used to build covert channels. Malicious DNS covert channels play an important role in data exfiltration and botnets and do great harm to the network environment. To detect DNS covert channels, researchers extract multiple features from different perspectives of DNS traffic. At present, many detection methods using machine learning are based on manual features, which usually include complex data preprocessing and feature extraction. Additionally, these methods seriously rely on expert knowledge, and some potential features are hard to discover. To address these problems, we propose a DNS covert channel detection method using the LSTM model, which does not rely on feature engineering. First, we use the FQDNs of DNS packets as the input and implement an end-to-end detection approach using LSTM. Then, we filter the detection results of the LSTM model with the grouped filtering method to further reduce the false positive rate. Using the packets from the Internet and the packets generated by running different DNS covert channel tools, we construct our datasets, in which generalization test datasets are included in addition to the FQDN and the DNS packet datasets for model training. Our method achieves an accuracy rate of 99.38% on the test dataset and a recall rate of 98.52% on the generalization test dataset, which are better than the state-of-the-art methods. This method is also tested in a real network environment and has detected multiple malicious DNS covert channel events. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

DNS is a basic network protocol utilized for creating covert channels, with malicious DNS covert channels causing significant harm in data exfiltration and botnets. Researchers extract multiple features and employ machine learning methods, including LSTM model, to detect and address these issues effectively without over-reliance on feature engineering.