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The dark web is a shadow area hidden in the depths of the Internet, which is difficult to access through common search engines. Because of its anonymity, the dark web has gradually become a hotbed for a variety of cyber-crimes. Although some research based on machine learning or deep learning has been shown to be effective in the task of analyzing dark web traffic in recent years, there are still pain points such as low accuracy, insufficient real-time performance, and limited application scenarios. Aiming at the difficulties faced by the existing automated dark web traffic analysis methods, a novel method named Dark-Forest to analyze the behavior of dark web traffic is proposed. In this method, firstly, particle swarm optimization algorithm is used to filter the redundant features of dark web traffic data, which can effectively shorten the training and inference time of the model to meet the real-time requirements of dark web detection task. Then, the selected features of traffic are analyzed and classified using the DeepForest model as a backbone classifier. The comparison experiment with the current mainstream methods shows that Dark-Forest takes into account the advantages of statistical machine learning and deep learning, and achieves an accuracy rate of 87.84%. This method not only outperforms baseline methods such as Random Forest, MLP, CNN, and the original DeepForest in both large-scale and small-scale dataset based learning tasks, but also can detect normal network traffic, tunnel network traffic and anonymous network traffic, which may close the gap between different network traffic analysis tasks. Thus, it has a wider application scenario and higher practical value.

Dark-Forest: Analysis on the Behavior of Dark Web Traffic via DeepForest and PSO Algorithm