  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.
