Web crawlers are special applications used to create a copy of all the visited web pages for later processing. They are mainly used for indexing websites to facilitate web search engines but are also used for web archiving, web mining, and web monitoring. Web crawling over dark web sites requires a set of initialisation steps. These include the instantiation services, the proxies, and the set-up of a keywords list for targeted look-ups to eliminate the search space in the Dark Web to the concepts which refer to cybersecurity incidents (i.e. hacks, SQL injection, DDoS attacks, etc.), email accounts (i.e. pawned email accounts for breached corporate data, job titles, names, phone numbers, physical addresses, social media profiles, etc.), an organisations’s leaked corporate information and their email servers (i.e. blacklisted email servers or blocked web services). After the process of collecting the data from the Dark Web HTML sites, we store and index them through Elasticsearch[1]. When we retrieve the HTML page, we convert the raw HTML text to cleaned text that contains all the HTML content and it is used as the input for a TFIDF Vectorizer[2]. After the data collection and harmonisation, we perform some text and graph analytics by using state-of-the-art Machine Learning algorithms, such as clustering. The intuition is to group the HTML pages that we collect based on their content similarity, monitor the critical score by means of top k queries of the cybersecurity terms frequency detected in the pages, as well as the way the URLs are linked in a concept graph map.