Darknet Traffic

Darknet traffic analysis focuses on identifying and classifying network communications originating from the dark web, aiming to detect illicit activities and enhance cybersecurity. Current research emphasizes developing robust machine learning models, including boosted decision trees, random forests, and convolutional neural networks, to classify darknet traffic and improve the accuracy of detection, while also addressing challenges like class imbalance and adversarial attacks. Furthermore, researchers are exploring natural language processing techniques, such as specialized language models trained on dark web data, to analyze the textual content of darknet websites and improve the understanding of dark web activities. These advancements contribute to improved threat detection and proactive cyber threat intelligence.