XTS: A Hybrid Framework to Detect DNS-Over-HTTPS Tunnels Based on XGBoost and Cooperative Game Theory

Mungwarakarama Irénée (), Yichuan Wang (), Xinhong Hei (), Xin Song, Jean Claude Turiho and Enan Muhire Nyesheja
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Mungwarakarama Irénée: School of Computer Science and Engineering, Xi’an University, Xi’an 710071, China
Yichuan Wang: School of Computer Science and Engineering, Xi’an University, Xi’an 710071, China
Xinhong Hei: School of Computer Science and Engineering, Xi’an University, Xi’an 710071, China
Xin Song: School of Computer Science and Engineering, Xi’an University, Xi’an 710071, China
Jean Claude Turiho: Computing and Information Science, University of Lay Adventists of Kigali, Kigali 6392, Rwanda
Enan Muhire Nyesheja: Computing and Information Science, University of Lay Adventists of Kigali, Kigali 6392, Rwanda

Mathematics, 2023, vol. 11, issue 10, 1-29

Abstract: This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation algorithm (SFE). The general aim of XTS is to reduce the number of features required to learn a particular dataset. It assumes that low-dimensional representation of data can improve computational efficiency and model interpretability whilst retaining a strong prediction performance. The efficiency of XTS was tested on a public dataset, and the results showed that by reducing the number of features from 33 to less than five, the proposed model achieved over 99.9% prediction efficiency. XTS was also found to outperform other benchmarked models and existing proof-of-concept solutions in the literature. The dataset contained data related to DNS-over-HTTPS (DoH) tunnels. The top predictors for DoH classification and characterization were identified using interactive SHAP plots, which included destination IP, packet length mode, and source IP. XTS offered a promising approach to improve the efficiency of the detection and analysis of DoH tunnels while maintaining accuracy, which can have important implications for behavioral network intrusion detection systems.

Keywords: DNS tunneling; DoH-based C2 covert channels; XGBoost; cooperative game theory; SHAP values; feature importance analysis; dimensionality reduction; imbalanced data; XAI (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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