A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence

• URL: http://arxiv.org/abs/2109.06932v1
• Date: Tue, 14 Sep 2021 19:26:08 GMT
• Title: A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence
• Authors: Paris Koloveas, Thanasis Chantzios, Christos Tryfonopoulos, Spiros Skiadopoulos
• Abstract summary: The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information. We present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web.
• Score: 1.1661238776379117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that -given the appropriate tools and methods-may be identified, crawled and subsequently leveraged to actionable cyber-threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state-of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.

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