Analyzing the threat landscape inside the dark web

The Dark Web is an encrypted subset of the deep web, whose content cannot be indexed by search engines. Dark Web pages can be accessed from private networks such as TOR (The Onion Routing), I2P (Invisible Internet Project) and Freenet. TOR is widely used by the Dark Web users in a domain defined by a .onion extension. Dark Web users can communicate with each other without using their identification. However, the anonymity of these users encourages them to perform illegal activities. This requires an immediate identification of imminent criminal threats and mitigation via algorithms, techniques and tools used to protect everyone from attacks inside the Dark Web.The aim is to make timely and pre-emptive detection of Dark Web threats before the Dark Web actor(s) can put their threats into action. The accuracy of the attacks on TOR network and the use of IoT and streaming technologies require agile algorithms to monitor the forums and to limit attacks. The methodology begins with a literature review, gap analysis and a research design using quantitative research methods such as comparative analysis of Dark Web forum datasets using data science techniques and an experimental research design involving machine learning and strategies for training and development of a model.After the gap analysis of the previous research methods, it is possible to try to extend or modify these algorithms or the applied techniques to see if those gaps can be closed. The findings and conclusion to this hybrid experimental research methodology will lead to a proposal on mitigating risks via a model for real-time detection, evaluation and response.