Dark web threat detection is no longer a luxury afforded to the largest corporations and government entities with vast financial resources. It is important even to small and midsize businesses that represent attractive targets to cybercriminals. Fortunately, advanced tools are making threat detection better with each passing day. One such tool is natural language processing (NLP).
The principle of NLP was originally developed around the idea of being able to translate texts via computer. Decades later, search engine operators discovered it could help them improve organic results for just about any query. And now, cybersecurity and data intelligence firms are turning to NLP to enhance dark web threat detection capabilities.
Finding Threats as Early as Possible
Dark web threat detection is all about finding threats as early as possible. In its earliest stages, a threat is more easily mitigated. Likewise, late-stage threat mitigation is more difficult. So it is in the best interests of nearly all organizations to proactively seek out threats in hopes of identifying them before they emerge.
What is NLP’s role here? It is one of assisting advanced threat detection software in the task of parsing human language for a better understanding of intent, ambiguity, and hidden meaning.
In a nutshell, NLP is able to analyze any given word separately or in the context of all the other words surrounding it. By comparing all the words in a given text against one another and historical data, an NLP system can artificially understand what is being discussed with amazing accuracy.
How It’s Helping Dark Web Threat Detection
NLP has long been used to power speech-to-text platforms. It is used by large language models (LLMs) to power generative AI tools. But how is it helping dark web threat detection, exactly? Consider the following three applications:
1. Extraction, Recognition, and Prediction
Tools equipped with NLP help fight cybercrime by way of terminology extraction, entity recognition, and emerging threat prediction. Terminology extraction allows for identifying domain-specific jargon encoded language. Entity recognition can extract information like IP addresses and crypto wallets. Finally, threat prediction identifies new attack vectors by analyzing keyword frequency and context.
2. Sentiment and Intent Analysis
NLP’s ability to artificially understand intent is critical to dark web threat detection. There are two things to consider in this regard, beginning with threat actor profiling. Certain NLP-power tools can predict a hacker’s emotional state thereby making it easier to identify an imminent threat.
Risk prioritization is the other consideration. NLP tools can identify high risk threats by analyzing language patterns that may be linked to a coordinated attack or aggressive intent by a single hacker.
3. Translation and Correlation
Finally, NLP enhances dark web threat detection through translation and correlation capabilities. On the translation front, NLP-equipped tools can automatically translate forum language for relevant insights regardless of a security team’s native language. This enhances global threat monitoring.
In the correlation front, NLP-equipped tools lend themselves very well to discovering similar usernames and user-generated content across multiple platforms – both darknet and otherwise. This facilitates better tracking of threat actor movements as individuals move around both the dark web and traditional internet.
A Tool Whose Time Has Come
DarkOwl is an industry leading provider of dark web investigation tools and threat detection data. They consider NLP a tool whose time has finally come in the cybersecurity realm.
NLP has long been used for other purposes. But now that it has made it to cybersecurity, it is revolutionizing how companies like DarkOwl identify threats in the earliest possible stages. And as it gets better, NLP will also improve as a tool against cybercrime.
