Analyzing Darknet Traffic Through Machine Learning and Neucube Spiking Neural Networks

Abstract

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels. Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging. This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity. Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats. Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98% accuracy from the random forest model and 84.31% accuracy from the spiking neural network. This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication. The proposed techniques lay the groundwork for improved threat intelligence, real-time monitoring, and resilient cyber defense systems against the evolving landscape of cyber threats.