Towards Detecting Influential Members and Critical Topics from Dark Web Forums: A Data Mining Approach

Abstract

Conventionally, the Internet consists of three parts: Surface, Deep, and Dark Webs. In the last two decades, a massive increase in illicit activities took place on the different platforms of the Dark Web. Moreover, social networks on Dark Web implicate extremism dissemination on a wide scale. In this paper, we propose an approach to generate textual patterns from discussions on Dark Web terrorist forums employing Data Mining techniques. The discovered patterns help identify the influential members and extract critical topics. We describe our system modules that perform data preprocessing, text preprocessing with TF-IDF weighting, outlier detection, clustering evaluation, clustering, and clustering validation, implemented with the RapidMiner tool. We apply K-Means as the Clustering method with different distance metrics, evaluate the clustering process using Elbow and Silhouette methods, and validate it using Davies-Bouldin Index. Furthermore, we investigate the effects of altering the distance metrics for outlier detection on the Clustering results.