The Dark Side of Language: Inside DarkBERT’s Journey into the Dark Web

South Korean researchers develop DarkBERT for analyzing the Dark Web

A team of researchers at the Korea Advanced Institute of Science & Technology has developed a language model to analyze the Dark Web, a hidden bunch of websites with illegal activities that are difficult for conventional search engines to index. The system, named DarkBERT, has been pre-trained on authentic documents acquired from the Dark Web, providing greater efficiency to navigate the hidden area and combat criminal activities.

The Dark Side of Internet

The Dark Web consists of barely 5% of the entire internet, but it draws in roughly three million users daily. Cybersecurity Ventures predicts that by 2025, the proceeds from global cybercrime will top $10 trillion. Criminals offer a laundry list of criminal digital services, including providing passwords to bank accounts and Social Security numbers, malware, and cyberattack packages that can bring down a company, a town or even a country.

According to James Scott, a senior fellow at the Institute for Critical Infrastructure Technology, “There’s a compounding and unraveling chaos that is perpetually in motion in the Dark Web’s toxic underbelly.”

Combating the Toxic Underbelly

DarkBERT is designed to tackle the extreme lexical and structural diversity of the Dark Web, which may be detrimental to building a proper representation of the domain, said researcher Youngjin Jin. Pre-trained language models based on Surface Web content are not ideal for extracting useful information due to the differences in the language used in the two domains. The team’s evaluation of DarkBERT showed that it outperformed the known pre-trained language models.

The team noted three key areas where DarkBERT proved to be effective, including ransomware leak detection, noteworthy thread detection in which potentially malicious threads were spotted, and threat keyword inference defined as “a set of keywords that are semantically related to threats and drug sales in the Dark Web.” Automating such analysis would significantly reduce the workload of security experts, especially with a language model trained in the unique vocabulary of the Dark Web, Jin said.

Importance of Learning the Language of Cybercriminals

Law enforcement has made progress in crushing illegal activity on the Dark Web. The first modern Dark Web marketplace, Silk Road, which made more than a billion dollars in illegal drug sales, was shut down by the FBI, and its creator was sentenced to life in prison. AlphaBay, which sold hundreds of millions of dollars worth of drugs and hacked data, was shut down by a multinational police effort. But those efforts were a drop in the bucket.

To achieve greater success, law enforcement must better learn the language of the cybercriminals. DarkBERT appears to be a good step in that direction. However, as with any technological advancement, there is the potential for it to be used for malevolent purposes. Therefore, the security measures must be taken to ensure that DarkBERT does not fall into the wrong hands.

Advice for Internet Users

As the number of cyber threats continues to rise, it is crucial for internet users to be vigilant. They must be careful with the information they share online and utilize effective security measures such as strong passwords and two-factor authentication. Furthermore, individuals must keep themselves up to date and aware of the latest threats and vulnerabilities, and use reliable antivirus software for their devices.

Internet security cannot only be ensured with technological advancements, but it requires the participation of all individuals to protect themselves from online threats.