In recent years, the clandestine nature of darknet activities has presented an escalating challenge to cybersecurity efforts, necessitating sophisticated methods for the detection and classification of network traffic associated with these covert operations. The system addresses the significant challenge of class imbalance within Darknet traffic datasets, where malicious traffic constitutes a minority, hindering effective discrimination between normal and malicious behavior. By leveraging boosting algorithms like AdaBoost and Gradient Boosting coupled with decision trees, this study proposes a robust solution for network traffic classification. Boosting algorithms ensemble learning corrects errors iteratively and assigns higher weights to minority class instances, complemented by the hierarchical structure of decision trees. The additional Feature Selection which is a preprocessing method by utilizing Information Gain metrics, Fisher's Score, and Chi-Square test selection for features is employed. Rigorous experimentation with diverse Darknet traffic datasets validates the efficacy of the proposed multistage classifier, evaluated through various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive solution for accurate detection and classification of Darknet activities.

翻译：近年来，暗网活动的隐蔽性对网络安全工作构成了日益严峻的挑战，亟需采用先进方法对与这些隐蔽操作相关的网络流量进行检测与分类。暗网流量数据集中普遍存在类别不平衡问题，其中恶意流量占少数，这阻碍了对正常与恶意行为的有效区分。本研究通过结合AdaBoost和梯度提升等提升算法与决策树，提出了一种鲁棒的解决方案。提升算法的集成学习通过迭代修正错误并为少数类样本分配更高权重，辅以决策树的分层结构，有效应对了类别不平衡问题。此外，还采用了基于信息增益度量、费希尔评分和卡方检验的特征选择作为预处理方法。通过对多种暗网流量数据集进行严格实验，并利用准确率、精确率、召回率和F1分数等多种性能指标进行评估，验证了所提出的多阶段分类器的有效性，为暗网活动的精确检测与分类提供了全面的解决方案。