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VCRT
AML cryptocurrency Cyber Attacks Cyber Criminals FBI KYC Ransomware VCRT

Cryptocurrency Exchanges Linked to Ransomware

 


Nine cryptocurrency exchange websites have been taken down by the FBI and the Ukrainian police in a daring joint operation. Cybercriminals and ransomware gangs use these websites to launder money for cybercriminals. This is because these websites facilitate money laundering by criminals operating online. Ukrainian prosecutors' offices and the Virtual Currency Response Team were also involved in the operation. 

Several virtual currency exchange services were seized by the FBI on Monday. These services may have been used by cybercriminals to launder money obtained through ransomware hacks. As a result of a collaboration between the FBI's Detroit Field Office and Ukrainian police, the Detroit FBI field office seized virtual currency exchanges used by criminals for anonymous transactions, the United States Department of Justice has announced. 

There is a press release that states that the FBI also received support from the Virtual Currency Response Team (VCRT), the National Police of Ukraine, and the regional prosecutors as a result of the 'crypto exchanges' operation. 

  1. 24xbtc.com 
  2. 100btc.pro 
  3. pridechange.com 
  4. 101crypta.com 
  5. uxbtc.com 
  6. trust-exchange.org 
  7. bitcoin24.exchange 
  8. paybtc.pro 
  9. owl.gold 
These websites allow you to anonymously buy Bitcoin, Ether, and other cryptocurrencies. They offer Russian and English exchange services with few Know Your Customer (KYC) or Anti-Money Laundering (AML) restrictions. In addition to online forums dedicated to criminal activity, websites are also advertised. 

These exchange servers have been shut down, and their domain names have been taken over by US authorities. Several exchanges were accused of offering anonymous cryptocurrency exchange services to website visitors. These visitors included cybercriminals, scammers, and many other bad actors, offering these services anonymously to site visitors. 

The FBI has accused these crypto exchanges of being used by cyber criminals, including scammers, ransomware operators, and hackers, for laundering money. Additionally, the FBI stated that these exchanges did not have a license. This acted as support for criminal activities under US laws. 

Two servers were confiscated. These servers were located in different parts of the world including the US, Ukraine, and several European countries. Cybercriminals used the exchanges to launder money from illegal activities, and the authorities are using the seized infrastructure to identify and track down those hackers.

It should be noted that both the English and Russian-language exchanges that offered similar services and avoided money laundering were censured by the FBI for the lack of anti-money laundering measures and the collection of Customer knowledge information, or none at all. The FBI claims that these sorts of unlicensed, rogue exchanges are one of the most critical hubs of the cybercrime ecosystem. 

Users have been able to convert their cryptocurrency into coins that are more difficult to track down on websites that have been seized anonymously. Hackers disguised the source of the money they stole and avoided detection by law enforcement agencies.

There is a lot of variety on these sites. Users can get live help and instructions in both Russian and English covering a wide range of cybercrime communities. 

The FBI's announcement indicates that noncompliant virtual currency exchanges that operate in violation of the United States Code, Sections 1960 and 1956, act as hubs for cybercrime. They have lax anti-money laundering programs and collect little information about their customers. These exchanges are significant cybercrime centers.

A search was conducted at the home of former FTX executive Ryan Salame early this month. This was part of the FBI's investigation into Salame's role as an advisor to Bankman-Fried at the time. 

During an operation conducted by the FBI and Ukrainian police, the FBI and Ukrainian police took down nine websites known as 'crypto exchanges'. These websites were well known for serving as money launderers for ransomware groups and cyber criminals. As part of an organized campaign, the daring action was undertaken by a cybercriminal who wanted to destroy the digital infrastructure that allows him to make money from his malicious actions by “interfering” with it and using it for his malicious goals. 


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NCSC
AML Cyber Security Machine learning NCSC

UK Government Releases New Machine Learning Guidance


Machine Learning and NCSC

The UK's top cybersecurity agency has released new guidance designed to assist developers and others identify and patch vulnerabilities in Machine Learning (ML) systems. 

GCHQ's National Cyber Security Centre (NCSC) has laid out together its principles for the security of machine learning for any company that is looking to reduce potential adversarial machine learning (AML). 

What is Adversarial Machine Learning (AML)?

AML attacks compromise the unique features of ML or AI systems to attain different goals. AML has become a serious issue as technology has found its way into a rising critical range of systems, finance, national security, underpinning healthcare, and more. 

At its core, software security depends on understanding how a component or system works. This lets a system owner inspect and analyze vulnerabilities, these can be reduced or accepted later. 

Sadly, it's difficult to deal with this ML. ML is precisely used for enabling a system that has self-learning, to take out information from data, with negligible assistance from a human developer.

ML behaviour and difficulty to interpret 

Since a model's internal logic depends on data, its behaviour can be problematic to understand, and at times is next to impossible to fully comprehend why it is doing what it is doing. 

This explains why ML components haven't undergone the same level of inspection as regular systems, and why some vulnerabilities can't be identified. 

According to experts, the new ML principles will help any organization "involved in the development, deployment, or decommissioning of a system containing ML." 

The experts have pointed out some key limitations in ML systems, these include:

  • Dependence on data: modifying training data can cause unintended behaviour, and the threat actors can exploit this. 
  • Opaque model logic: developers sometimes can't understand or explain a model's logic, which can affect their ability to reduce risk.
  • Challenges verifying models: it is almost impossible to cross-check if a model will behave as expected under the whole range of inputs to which it might be a subject, and we should note that there can be billions of these. 
  • Reverse engineering models and training data can be rebuilt by threat actors to help them in launching attacks. 
  • Need for retraining: Many ML systems use "continuous learning" to improve performance over time, however, it means that security must be reassessed every time a new model version is released. It can be several times a day. 

In the NCSC, the team recognises the massive benefits that good data science and ML can bring to society, along with cybersecurity. The NCSC wants to make sure these benefits are recognised. 








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