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.

Customer churn is perhaps the biggest challenge in telecommunication industry. Customer churn is the term which indicates the customer who is in the stage to leave the company or Customer churn means a customer can leave their service or service provider and move to another service or service provider. This rate is increasing day by day in all telecommunication companies. Data mining is one of the techniques which provide different methods and application to find out those customers who are going to churn and how to prevent them. The aim of this paper is to compare different algorithms of Data Mining which have been used for making distinction between customers into non churn and churn, so that appropriate steps can be taken into consideration in order to retain the churn customers to the company as customers are more valuable to the survival and development of the company.