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machine

In the Ural region of Russia, 10,000 mining machines were seized from an illegal mining site, with electricity cost losses amounting to nearly 1 billion rubles

According to Bits.media, a large illegal cryptocurrency mining operation was discovered in the city of Nizhny Tagil in Sverdlovsk Oblast, Russia, and the nearby city of Kushva. The mining operation was hidden in an abandoned industrial park and deployed about 10,000 mining machines, which were dismantled by a joint operation of the Federal Security Service of the Russian Federation, the police, and the power company.Local power companies estimate that the losses caused by the long-term illegal electricity usage of this mining operation amount to nearly 1 billion rubles (approximately 12.7 million USD). Investigators stated that its electricity consumption was sufficient to meet the lighting needs of a small city. Law enforcement has arrested three suspects, who are currently under house arrest and are being investigated for "causing property damage through deception or abuse of trust." Under Russian law, those involved could face up to 5 years in prison.Investigations revealed that the operators of the mining site accessed the power grid through intermediaries and allegedly tampered with electricity meter data to cover up the actual electricity usage. Law enforcement agencies stated that the actual electricity consumption of the mining operation was about twice the approved quota. The local energy department initially launched an investigation due to frequent voltage fluctuations, power outages, and equipment failures in the abandoned factory area, ultimately pinpointing the location of the mining operation. A local television station also produced a documentary titled "Mining" to document this operation.

Ningbo Customs Anti-Smuggling Bureau has cracked a series of smuggling cases involving virtual currency mining machines, seizing over 400 "mining machines."

According to Zhejiang Daily, recently, the Ningbo Customs Anti-Smuggling Bureau successfully dismantled multiple criminal gangs smuggling virtual currency "mining machines" through in-depth operations and meticulous investigations, effectively cutting off an illegal industrial chain.Previously, during a routine inspection of a batch of imported express shipments declared as "industrial blockers," Ningbo Customs discovered that the actual goods did not match the declaration and were, in fact, virtual currency "mining machines." Customs officers quickly transferred this lead to the anti-smuggling department. After receiving the report, the Ningbo Customs Anti-Smuggling Bureau immediately assembled a task force by drawing on elite personnel, and through data analysis and clue investigation, gradually clarified the organizational structure and operational model of the criminal network. When the timing was right, they decisively struck, simultaneously conducting net-seizing operations in Dongguan, Shenzhen, and other locations, successfully dismantling multiple smuggling gangs of mining machines.Upon investigation, it was confirmed that this series of cases seized over 400 illegal entry mining machines of brands such as Ant L9 and Ice River KS3. The investigation revealed that the smuggling gang led by Liao, in order to make illegal profits, procured "mining machines" from overseas suppliers, disassembled the whole machines, and misreported the product names to smuggle them into the country through international express channels from ports in Ningbo, Guangzhou, and other places. After the goods entered the country, the gang reassembled them, either selling them directly domestically or transporting them to hidden "mining sites" in Xinjiang, Hunan, and other places to engage in illegal virtual currency "mining" activities. At the same time, they utilized virtual currencies like USDT for cross-border payment settlements to evade financial supervision.

Coinbase upgrades its anti-fraud system, integrating machine learning with a rules engine, reducing response time to a few hours

Coinbase stated that it is optimizing the rule creation process in its anti-fraud system by integrating machine learning models with a rules engine, achieving more efficient risk management. It also proposed a dual-track strategy of "models responsible for long-term defense, rules responsible for rapid response," and built a unified framework to create a feedback loop between the two: rules are used to capture new types of fraud and train the model in reverse, thereby continuously enhancing overall defense capabilities.In terms of specific optimizations, Coinbase has transformed the previously manual rule creation process into a data-driven and automated recommendation system by restructuring data, automating schema evolution, and introducing notebook-based analytical tools, significantly improving efficiency. Among these improvements, the performance of rule backtesting has increased by more than 10 times, and the overall response time has been reduced from several days to a few hours. Additionally, the new system uses machine learning to recommend parameters, helping to reduce false positive rates while combating fraud and minimizing the impact on normal users. Coinbase indicated that the next step will be to advance event-driven automatic rule generation and explore the "one-click conversion" of efficient rules into model features, further moving towards an automated risk management system.
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