Imagine working with a security team assigned to protecting a high-profile politician. Your job would be more investigative than anything else. You would be tasked with hunting down information that could indicate an imminent threat. The information you provide could mean the difference between success and failure. As it turns out, dark web threat detection is very similar.
Successful threat detection starts with data. And in fact, data collection is the foundation. Without the right data and appropriate analytics applied to it, cybersecurity teams are left to make educated guesses.
With that in mind, data crawling and collection is an integral part of dark web threat detection. Knowing how both play a role in detecting cyber threats provides a good foundation for understanding the threat detection principle and its application for maintaining cybersecurity.
Data Crawling and Collection in a Nutshell
Dark web data crawling and collection is a comprehensive practice that relies on specialized techniques and software tools to navigate hidden and encrypted networks. Note that the dark web is made up of such networks. They are extremely difficult to find and access. You need to know what you are doing to get into them.
Think of data crawling as similar to the mining practice of collecting large volumes of ore. Data collection is more like separating the ore from the valuable minerals you really want. Employing both data crawling and collection results in actionable data that security teams can do something with.
Core Techniques for Mining Useful Information
Security teams rely on a few core techniques to turn large volumes of data into actionable dark web threat detection. Let us start with web crawling and scraping. Organizations like DarkOwl employ the same techniques as internet search engines.
DarkOwl’s dark web threat detection platform continually crawls targeted networks through a process that systematically discovers and extracts data. Data is taken from dark web marketplaces, forums, etc. while bypassing security controls like CAPTCHA.
The platform scrapes as much data as it can. All the while, it employs the latest strategies for evading security checks. Ironically, they do to threat actors what threat actors do to their victims – at least in the sense of gathering data threat actors don’t want them to gather.
There are two things to consider in terms of crawling and scraping:
- Site Identification – Dark web intelligence providers seek to identify relevant websites through reverse search methods. Those websites are then ranked by relevance before data collection is initiated.
- Site Exploration – Crawling and scraping a site allows platforms to prioritize discovered links based on value. Classified forms are deployed to extract structured data.
Between the two processes, threat intelligence platforms can zero in on the most credible and serious threats. Security teams can then prioritize threats based on their individual risk postures.
Leveraging Data for Dark Web Threat Detection
Once sites have been scraped and data has been collected, advanced algorithms process the data in hopes of spitting out something useful. The first step is something known as data cleaning. Raw data is processed to remove duplicates and noise. It is then compared to historical data for further refinement.
After all that, useful data is deployed. All the data, whether immediately useful or not, is also stored. It becomes historical data that can be leveraged for future analytics. It is compared to future data to help security teams identify emerging threats.
Dark web threat detection is heavily reliant on data. Without data, randomly guessing would be the only way security teams could try to identify emerging threats. That is no way to protect a network.
