Detecção de eventos em fóruns da Dark Web e da Surface Web usando algoritmos de aprendizado de máquina

Abstract

The detection of malicious posts in Dark Web and Surface Web forums is widely utilized in the field of digital security. Analyzing these posts can aid in the development of better IDSs, the identification of cyber attacks, the discovery of new types of threats, and other benefits. Many forms of attacks can be proactively prevented by searching forum posts for topics related to digital security. To achieve this, it is necessary to identify emerging events in posts with new topics that may contain keywords of interest from a digital security perspective. This work aims to propose an algorithm for detecting novel events and analyzing publications from Dark Web and Surface Web forums over time. Novel events are defined as emerging topics of interest within the cybersecurity domain. To assess the significance of the detected events, a malicious post classifier was employed to categorize posts as having high, medium, or low relevance. The proposed algorithm demonstrated its potential by identifying 26.071 new events from a dataset of 30.584 analyzed posts, among which approximately 4% were classified as highly relevant.