Classification of Darknet Traffic Using Machine Learning

Abstract

Darknet network traffic, characterized by their
hidden nature and association with illicit activities, pose
significant challenges for network monitoring and
security. To address these challenges, this study explores
the classification of darknet network traffic using
machine learning algorithms. A labeled dataset
comprising network traffic captured from darknet
environments is collected and preprocessed to extract
relevant features. Various machine learning algorithms,
including decision trees, random forests, AdaBoost,
KNN, Naïve Bayes, Simple CART, Gradient Boosting
are evaluated for their effectiveness in classifying
darknet traffic. The dataset is divided into training and
testing sets, and the selected machine learning models
are trained using the training set. The models learn the
underlying patterns and relationships between network
traffic features followed by relevant classification. The
models are then evaluated on the testing set to measure
their classification performance in terms of accuracy,
precision, recall, and F1-score.The results demonstrate
the efficacy of machine learning algorithms in
classifying multilayered darknet network traffic. The
selected models achieve high accuracy and show
promising potential in identifying various types of
malicious or illicit activities within the darknet. The
study also discusses the interpretability of the models
and the insights gained from feature importance
analysis, contributing to a better understanding of
darknet network behavior. The findings from this
research have implications for network security, law
enforcement, and cyber security operations, enabling
proactive monitoring and detection of darknet activities.
The developed classification models can be integrated
into real-time traffic analysis systems, aiding in the
identification and mitigation of threats originating from
the darknet.