The sanctions against Russia-Based Hydra and currency exchange Garantex, dark markets published on the Treasury Department’s website, “send a message today to criminals that you cannot hide on the darknet market or their forums, and you cannot hide in Russia or anywhere else in the world,” U.S.

Treasury Secretary Janet Yellen said.

(Reporting by Rami Ayyub Editing by Chris Reese)

The post U.S. imposes sanctions on Russian darknet market and crypto exchange appeared first on The News Max.

The suspects arrested included several high-profile targets, involved in buying or selling illegal goods online, dark web markets Europol said today.

Operation Dark HunTOR also recovered millions of pounds in cash and , as well as drugs and guns. 

The bust stems from a German-led police sting earlier this year taking down the ‘world’s largest’ darknet marketplace.

darknet market markets are e-commerce sites designed to lie beyond the reach of regular search engines and are popular with criminals, as buyers and sellers are largely untraceable. 

Dark HunTOR, ‘was composed of a series of separate but complementary actions in Australia, Bulgaria, France, Germany, Italy, the Netherlands, Switzerland, the United Kingdom, dark Web market links and the United States,’ the Hague-based Europol said.

In the United States alone, police arrested 65 people, while 47 were held in Germany, 24 in Britain, and four each in Italy and the Netherlands, among others.

A number of those arrested ‘were considered high-value targets’ by Europol.

Law agents also confiscated 26.7 million euros (£22.45million) in cash and virtual currencies, as well as 45 guns and 516lbs of drugs, including 25,000 ecstasy pills.

Italian police also shut down the ‘DeepSea’ and dark darknet market link ‘Berlusconi’ marketplaces, ‘which together boasted over 100,000 announcements of illegal products’, said Europol, which coordinated the operation together with its twin judicial agency Eurojust.

German police in January closed down the ‘DarkMarket’ online marketplace, used by its alleged operator, an Australian, to facilitate the sale of drugs, stolen credit card data and malware.

Europol said the arrest of the alleged operator, caught near the German-Danish border at the time, and the seizure of the criminal infrastructure provided ‘investigators across the world with a trove of evidence’.

German prosecutors at the time said DarkMarket came to light in the course of a major investigation against the web-hosting service Cyberbunker, located in a former NATO bunker in southwest Germany.

Europol’s European Cybercrime Centre EC3 has since been compiling intelligence packages to identify the key targets, the continent’s policing agency said.

The secret ‘darknet market‘ includes websites that can be assessed only with specific software or authorisations, ensuring anonymity for users.

They have faced increased pressure from international law enforcement in recent months.

‘The point of operations such as this is to put criminals operating on the dark web on notice (that) the law enforcement community has the means and global partnerships to unmask them and hold them accountable for their illegal activities,’ Europol deputy director of operations Jean-Philippe Lecouffe said.

Rolf van Wegberg, cybercrime investigator at the TU Delft university said the operation signalled a break in the trend of recent police actions against suspected online criminals.

‘This kind of operations in the past looked at arresting the controllers of these marketplaces, we now see police services targeting the top sellers,’ he told investigative journalists at the Dutch KRO-NCRV public broadcaster.

A press conference about the operation has been set for dark markets 10am local time (2pm GMT) in Washington with the Department of Justice. 

The post One of the biggest ever dark web police stings leads to 150 arrests appeared first on The News Max.

This is part of our  about how innovators are thinking up new ways to make you — and the world around you — smarter. 

---

“Are you a hacker?”

A Las Vegas driver asks me this after I tell him I’m headed to Defcon at Caesars Palace. I wonder if his sweat isn’t just from the 110℉ heat blasting the city.

All week, a cloud of paranoia looms over Las Vegas, as hackers from around the world swarm Sin City for Black Hat and Defcon, two back-to-back cybersecurity conferences taking place in the last week of July. At Caesars Palace, where Defcon is celebrating its 25th anniversary, the UPS store posts a sign telling guests it won’t accept printing requests from USB thumb drives. You can’t be too careful with all those hackers in town.

Everywhere I walk I see hackers — in tin-foiled fedoras, wearing . Mike Spicer, a security researcher, carries a 4-foot-high backpack holding a “Wi-Fi cactus.” Think wires, antennas, colored lights and 25 Wi-Fi scanners that, in seven hours, captured 75 gigabytes of data from anyone foolish enough to use public Wi-Fi. I see a woman thank him for holding the door open for her, all while his backpack sniffs for unencrypted passwords and personal information it can grab literally out of thin air.

You’d think that, with all the potential threats literally walking about town, Vegas’ director of technology and innovation, Mike Sherwood, dark web market links would be stressed out. It’s his job to protect thousands of smart sensors around the city that could jam traffic, blast water through pipes or cause a blackout if anything goes haywire.

And yet he’s sitting right in front of me at Black Hat, smiling.

His entire three-person team, in fact, is at Black Hat so they can learn how to stave off future attacks. Machine learning is guarding Las Vegas’ network for them.

Broadly speaking, artificial intelligence refers to machines carrying out jobs that we would consider smart. Machine learning is a subset of AI in which computers learn and adapt for themselves.

Now a number of cybersecurity companies are turning to machine learning in an attempt to stay one step ahead of professionals working to steal industrial secrets, disrupt national infrastructures, hold computer networks for ransom and even influence elections. Las Vegas, which relies on machine learning to keep the bad guys out, offers a glimpse into a future when more of us will turn to our AI overlords for protection.

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Man and machine

At its most basic, machine learning for security involves feeding massive amounts of data to the AI program, which the software then analyzes to spot patterns and recognize what is, and isn’t, a threat. If you do this millions of times, the machine becomes smart enough to prevent intrusions and malware on its own.

Theoretically.

Machine learning naysayers argue that hackers can write malware to trick AI. Sure the software can learn really fast, but it stumbles when it encounters data its creators didn’t anticipate. Remember how trolls turned ? It makes a good case against relying on AI for cybersecurity, where the stakes are so high.

Even so, that has protected Las Vegas’ network and thousands of sensors for the last 18 months.

Since last February, Darktrace has defended the city from cyberattacks, around the clock. That comes in handy when you have only three staffers handling cybersecurity for people, 3,000 employees and thousands of online devices. It was worse when Sherwood joined two years ago.

“That was the time where we only had one security person on the team,” Sherwood tells me. “That was when I thought, ‘I need help and I can’t afford to hire more people.'”

He’d already used Darktrace in his previous job as deputy director of public safety and city technology in Irvine, California, and he thought the software could help in Las Vegas. Within two weeks, Darktrace found malware on Las Vegas’ network that was sending out data.

“We didn’t even know,” Sherwood says. “Traditional scanners weren’t picking it up.”  

Pattern recognition

I’m standing in front of a tattoo parlor in , a little more than 4 miles from Caesars Palace. Across the street, I see three shuttered stores next to two bail bonds shops.

I’m convinced the taxi driver dropped me off at the wrong location.

This is supposed to be Vegas’ $1 million Innovation District project? Where are the in the area? Or the ?

I look again at the Innovation District map on my phone. I’m in the right place. Despite the rundown stores, trailer homes and empty lots, this corner of downtown Vegas is much smarter than it looks.

That’s because hidden on the roads and inside all the streetlights, traffic signals and pipes are thousands of sensors. They’re tracking the air quality, controlling the lights and water, counting the cars traveling along the roads, and providing Wi-Fi.

Officials chose the city’s rundown area to serve as its Innovation District because they wanted to redevelop it, with help from technology, Sherwood says. There’s just one problem: darkmarkets All those connected devices are potential targets for a cyberattack. That’s where Darktrace comes in.

Sherwood willingly banks on Darktrace to protect the city’s entire network because the software comes at machine learning from a different angle. Most machine learning tools rely on brute force: cramming themselves with thousands of terabytes of data so they can learn through plenty of trial and error. That’s how IBM’s Deep Blue computer learned to defeat Garry Kasparov, the world chess champion, dark web darknet market links in a best-of-seven match in 1997. In the security world, that data describes malware signatures — essentially algorithms that identify specific viruses or worms, for instance.

Darktrace, in contrast, doesn’t look at a massive database of malware that’s come before. Instead, it looks for patterns of human behavior. It learns within a week what’s considered normal behavior for users and sets off alarms when things fall out of pattern, like when someone’s computer suddenly starts encrypting loads of files.

Rise of the machines?

Still, it’s probably too soon to hand over all security responsibilities to artificial intelligence, says  , a security professor and director dark darknet market list of Carnegie Mellon University’s CyLab Security and Privacy Institute. He predicts it’ll take at least 10 years before we can safely use AI to keep bad things out.

“It’s really easy for AI to miss things,” Brumley tells me over the phone. “It’s not a perfect solution, and you still need people to make important choices.”

Brumley’s team last year built an AI machine that won beating out other AI entries. A few days later, their contender took on some of the world’s best hackers at Defcon. They came in last.

Sure, machines can help humans fight the scale and speed of attacks, but it’ll take years before they can actually call the shots, says Brumley.

That’s because the model for AI right now is still data cramming, which — by today’s standards — is actually kind of dumb.

But it was still good enough to , making him the de facto poster child for man outsmarted by machine.

“I always remind people it was a rematch, because I won the first one,” he tells me, chuckling, while sitting in a room at Caesars Palace during Defcon. Today Kasparov, 54, is the which is why he’s been giving talks around the country on why humans need to work with AI in cybersecurity.  

He tells me machines can now learn too fast for humans to keep up, no matter if it’s chess or cybersecurity. “The vigilance and the precision required to beat the machine — it’s virtually impossible to reach in human competition,” Kasparov says.

Nobody’s perfect

About two months before Defcon, I’m at Darktrace’s headquarters in New York, where company executives show me how the system works.

On a screen, I see connected computers and printers sending data to Darktrace’s network as it monitors for behavior that’s out of the ordinary.  

“For example, Sue doesn’t usually access this much internal data,” Nancy Karches, Darktrace’s sales manager, tells me. “This is straying from Sue’s normal pattern.” So Darktrace shuts down an attack most likely waged by another machine.

“When you have machine-based attacks, the attacks are moving at a machine speed from one to the other,” says Darktrace CEO Nicole Eagan. “It’s hard for humans to keep up with that.”

But what happens when AI becomes the norm? When everyone’s using AI, says Brumley, hackers will turn all their attention on finding the machines’ flaws — something they’re not doing yet.

“We’ve seen again and again, the reason new solutions work better is because attackers aren’t targeting its weaknesses,” he says. “As soon as it became popular, it started working worse and worse.”

About 60 percent of cybersecurity experts at Black Hat believe hackers will use AI for attacks by 2018, according to a survey from the security company Cylance.

“Machine learning security is not foolproof,” says Hyrum Anderson, principal data scientist at cybersecurity company Endgame, who and their tools. Anderson expects AI-based malware will rapidly make thousands of attempts to find code that the AI-based security misses.

“The bad guy can do this with trial and error, and it will cost him months,” Anderson says. “The bot can learn to do this, and it will take hours.”

Anderson says he expects cybercriminals will eventually sell AI malware on darknet market lists markets to wannabe hackers.

For now, Sherwood feels safe having the city protected by an AI machine, which has shielded Las Vegas’ network for the past year. But he also realizes a day will come when hackers could outsmart the AI. That’s why Sherwood and his Las Vegas security team are at Black Hat: to learn how to use human judgment and creativity while the machine parries attacks as rapidly as they come in.

Kasparov has been trying to make that point for the last 20 years. He sees machines doing about 80 percent to 90 percent of the work, but he believes they’ll never get to what he calls “that last decimal place.”

“You will see more and more advanced destruction on one side, and that will force you to become more creative on the positive side,” he tells me.

“Human creativity is how we make the difference.”

: Reporters’ dispatches from the field on tech’s role in the global refugee crisis. 

: CNET hunts for innovation outside the Silicon Valley bubble. 

The post Stopping cyberattacks. No human necessary appeared first on The News Max.

This is part of our  about how innovators are thinking up new ways to make you — and the world around you — smarter. 

---

“Are you a hacker?”

A Las Vegas driver asks me this after I tell him I’m headed to Defcon at Caesars Palace. I wonder if his sweat isn’t just from the 110℉ heat blasting the city.

All week, a cloud of paranoia looms over Las Vegas, darkmarket as hackers from around the world swarm Sin City for Black Hat and Defcon, two back-to-back cybersecurity conferences taking place in the last week of July. At Caesars Palace, where Defcon is celebrating its 25th anniversary, the UPS store posts a sign telling guests it won’t accept printing requests from USB thumb drives. You can’t be too careful with all those hackers in town.

Everywhere I walk I see hackers — in tin-foiled fedoras, wearing . Mike Spicer, a security researcher, carries a 4-foot-high backpack holding a “Wi-Fi cactus.” Think wires, antennas, colored lights and 25 Wi-Fi scanners that, in seven hours, captured 75 gigabytes of data from anyone foolish enough to use public Wi-Fi. I see a woman thank him for holding the door open for her, all while his backpack sniffs for unencrypted passwords and personal information it can grab literally out of thin air.

You’d think that, with all the potential threats literally walking about town, Vegas’ director of technology and innovation, Mike Sherwood, would be stressed out. It’s his job to protect thousands of smart sensors around the city that could jam traffic, blast water through pipes or cause a blackout if anything goes haywire.

And yet he’s sitting right in front of me at Black Hat, smiling.

His entire three-person team, in fact, is at Black Hat so they can learn how to stave off future attacks. Machine learning is guarding Las Vegas’ network for them.

Broadly speaking, artificial intelligence refers to machines carrying out jobs that we would consider smart. Machine learning is a subset of AI in which computers learn and adapt for themselves.

Now a number of cybersecurity companies are turning to machine learning in an attempt to stay one step ahead of professionals working to steal industrial secrets, disrupt national infrastructures, hold computer networks for ransom and even influence elections. Las Vegas, which relies on machine learning to keep the bad guys out, offers a glimpse into a future when more of us will turn to our AI overlords for protection.

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Man and machine

At its most basic, machine learning for security involves feeding massive amounts of data to the AI program, which the software then analyzes to spot patterns and recognize what is, and isn’t, a threat. If you do this millions of times, the machine becomes smart enough to prevent intrusions and malware on its own.

Theoretically.

Machine learning naysayers argue that hackers can write malware to trick AI. Sure the software can learn really fast, but it stumbles when it encounters data its creators didn’t anticipate. Remember how trolls turned ? It makes a good case against relying on AI for cybersecurity, where the stakes are so high.

Even so, that has protected Las Vegas’ network and thousands of sensors for the last 18 months.

Since last February, Darktrace has defended the city from cyberattacks, around the clock. That comes in handy when you have only three staffers handling cybersecurity for people, 3,000 employees and thousands of online devices. It was worse when Sherwood joined two years ago.

“That was the time where we only had one security person on the team,” Sherwood tells me. “That was when I thought, ‘I need help and I can’t afford to hire more people.'”

He’d already used Darktrace in his previous job as deputy director of public safety and city technology in Irvine, California, and he thought the software could help in Las Vegas. Within two weeks, dark websites Darktrace found malware on Las Vegas’ network that was sending out data.

“We didn’t even know,” Sherwood says. “Traditional scanners weren’t picking it up.”  

Pattern recognition

I’m standing in front of a tattoo parlor in , a little more than 4 miles from Caesars Palace. Across the street, I see three shuttered stores next to two bail bonds shops.

I’m convinced the taxi driver dropped me off at the wrong location.

This is supposed to be Vegas’ $1 million Innovation District project? Where are the in the area? Or the ?

I look again at the Innovation District map on my phone. I’m in the right place. Despite the rundown stores, trailer homes and empty lots, this corner of downtown Vegas is much smarter than it looks.

That’s because hidden on the roads and inside all the streetlights, traffic signals and pipes are thousands of sensors. They’re tracking the air quality, controlling the lights and water, counting the cars traveling along the roads, and providing Wi-Fi.

Officials chose the city’s rundown area to serve as its Innovation District because they wanted to redevelop it, with help from technology, Sherwood says. There’s just one problem: All those connected devices are potential targets for a cyberattack. That’s where Darktrace comes in.

Sherwood willingly banks on Darktrace to protect the city’s entire network because the software comes at machine learning from a different angle. Most machine learning tools rely on brute force: cramming themselves with thousands of terabytes of data so they can learn through plenty of trial and error. That’s how IBM’s Deep Blue computer learned to defeat Garry Kasparov, the world chess champion, in a best-of-seven match in 1997. In the security world, that data describes malware signatures — essentially algorithms that identify specific viruses or dark darknet market list worms, for instance.

Darktrace, in contrast, doesn’t look at a massive database of malware that’s come before. Instead, it looks for patterns of human behavior. It learns within a week what’s considered normal behavior for users and sets off alarms when things fall out of pattern, like when someone’s computer suddenly starts encrypting loads of files.

Rise of the machines?

Still, it’s probably too soon to hand over all security responsibilities to artificial intelligence, says  , a security professor and director of Carnegie Mellon University’s CyLab Security and Privacy Institute. He predicts it’ll take at least 10 years before we can safely use AI to keep bad things out.

“It’s really easy for AI to miss things,” Brumley tells me over the phone. “It’s not a perfect solution, and you still need people to make important choices.”

Brumley’s team last year built an AI machine that won beating out other AI entries. A few days later, their contender took on some of the world’s best darknet markets hackers at Defcon. They came in last.

Sure, machines can help humans fight the scale and speed of attacks, but it’ll take years before they can actually call the shots, says Brumley.

That’s because the model for AI right now is still data cramming, which — by today’s standards — is actually kind of dumb.

But it was still good enough to , making him the de facto poster child for man outsmarted by machine.

“I always remind people it was a rematch, because I won the first one,” he tells me, chuckling, while sitting in a room at Caesars Palace during Defcon. Today Kasparov, 54, is the which is why he’s been giving talks around the country on why humans need to work with AI in cybersecurity.  

He tells me machines can now learn too fast for humans to keep up, no matter if it’s chess or cybersecurity. “The vigilance and the precision required to beat the machine — it’s virtually impossible to reach in human competition,” Kasparov says.

Nobody’s perfect

About two months before Defcon, I’m at Darktrace’s headquarters in New York, where company executives show me how the system works.

On a screen, I see connected computers and best darknet markets printers sending data to Darktrace’s network as it monitors for behavior that’s out of the ordinary.  

“For example, Sue doesn’t usually access this much internal data,” Nancy Karches, Darktrace’s sales manager, tells me. “This is straying from Sue’s normal pattern.” So Darktrace shuts down an attack most likely waged by another machine.

“When you have machine-based attacks, the attacks are moving at a machine speed from one to the other,” says Darktrace CEO Nicole Eagan. “It’s hard for humans to keep up with that.”

But what happens when AI becomes the norm? When everyone’s using AI, says Brumley, hackers will turn all their attention on finding the machines’ flaws — something they’re not doing yet.

“We’ve seen again and again, the reason new solutions work better is because attackers aren’t targeting its weaknesses,” he says. “As soon as it became popular, it started working worse and worse.”

About 60 percent of cybersecurity experts at Black Hat believe hackers will use AI for attacks by 2018, according to a survey from the security company Cylance.

“Machine learning security is not foolproof,” says Hyrum Anderson, principal data scientist at cybersecurity company Endgame, who and their tools. Anderson expects AI-based malware will rapidly make thousands of attempts to find code that the AI-based security misses.

“The bad guy can do this with trial and error, and it will cost him months,” Anderson says. “The bot can learn to do this, and it will take hours.”

Anderson says he expects cybercriminals will eventually sell AI malware on darknet market markets to wannabe hackers.

For now, Sherwood feels safe having the city protected by an AI machine, which has shielded Las Vegas’ network for the past year. But he also realizes a day will come when hackers could outsmart the AI. That’s why Sherwood and his Las Vegas security team are at Black Hat: to learn how to use human judgment and creativity while the machine parries attacks as rapidly as they come in.

Kasparov has been trying to make that point for the last 20 years. He sees machines doing about 80 percent to 90 percent of the work, but he believes they’ll never get to what he calls “that last decimal place.”

“You will see more and more advanced destruction on one side, and that will force you to become more creative on the positive side,” he tells me.

“Human creativity is how we make the difference.”

: Reporters’ dispatches from the field on tech’s role in the global refugee crisis. 

: CNET hunts for innovation outside the Silicon Valley bubble. 

The post Stopping cyberattacks. No human necessary appeared first on The News Max.

Frankfurt prosecutors said in a statement together with the Federal Criminal Police Office that in mid-April three German suspects, said to be the administrators of the “Boystown” platform, were arrested along with a German user.
One of the three main suspects was arrested in Paraguay.

They also searched seven buildings in connection with the porn ring in mid-April in Germany.

The authorities said the platform was “one of the world’s biggest child pornography darknet market platforms” and darknet market lists had been active at least since 2019.

Pedophiles used it to exchange and watch pornography of children and toddlers, most of them boys, dark darknet market link from all over the world.

Prosecutors wrote that they found “images of most severe sexual abuse of toddlers” among the photos and video material.

“The platform had several forums and chats – the illegal pictures and videos were kept in the forums; in the chats, the members could communicate,” prosecutor Julia Bussweiler said.
“There were several language channels to facilitate the communication.”

A German police task force investigated the platform, its administrators and users for months in cooperation with Europol and law enforcement authorities from the Netherlands, Sweden, Australia, the United States and Canada, the statement said.

The three main suspects were a 40-year-old man from Paderborn, a 49-year-old man from Munich and a 58-year-old man from northern Germany who had been living in Paraguay for many years, darkmarket 2023 the prosecutors’ statement said.

They worked as administrators of the site and gave advice to members on how to evade law enforcement when using the platform for illegal child pornography.

A fourth suspect, a 64-year-old man from Hamburg, is accused of being one of the most active users of the platform having allegedly uploaded more than 3,500 posts.

Germany has requested the extradition of the suspect who was arrested in Paraguay.

No names were given in line with Germany privacy regulations.

After the raids in mid-April, the online platform was shut down.

Germany’s top security official thanked the authorities for their success.

“This investigative success has a clear message: Those who assault the weakest aren’t safe anywhere,” German Interior Minister Horst Seehofer said.

“That’s what investigators work for day and night, online and offline, globally.”

“We’ll do everything within our power to protect the kids from these disgusting crimes,” he added.

___

Christoph Noelting in Frankfurt, Germany, contributed to this story.

The post Germany busts international child porn site used by 400,000 appeared first on The News Max.

The post Massive dark web bust seizes $6.5 million from 179 alleged drug dealers appeared first on The News Max.

The darknet market had almost 500,000 users with 2,400 sellers, Europol said in a statement.
Transactions conducted on it in cryptocurrency were worth more than 140 million euros ($170 million).

“The vendors on the marketplace mainly traded all kinds of drugs and sold counterfeit money, stolen or counterfeit credit card details, anonymous SIM cards and malware,” Europol said.

Agencies from Australia, Denmark, Moldova, Ukraine, United Kingdom and the United States also took part in the operation, which Europol helped to coordinate.

darknet market lists markets are e-commerce sites designed to lie beyond the reach of regular search engines.

They are popular with criminals, as buyers and sellers are largely untraceable. Payments on the DarkMarket were made in bitcoin and monero.

The investigation was led by German prosecutors and an Australian citizen who is alleged to be the operator dark darknet market of DarkMarket was arrested near the German-Danish border last weekend, darknet market lists Europol said.
More that 20 servers were seized in Moldova and Ukraine.

(Reporting by Stephanie van den Berg; Editing by Angus MacSwan)

The post Europol says illegal marketplace "DarkMarket" taken offline appeared first on The News Max.

The post One-third of the people reading this are thieves appeared first on The News Max.

The post The largest dark web market for illegal goods is no more appeared first on The News Max.

NEW YORK, Jan 26 (Reuters) – Cybercriminals laundered $8.6 billion in cryptocurrencies last year, up 30% from 2020, according to a report from blockchain analysis firm Chainalysis released on Wednesday.

Overall, cybercriminals have laundered more than $33 billion worth of crypto since 2017, Chainalysis estimated, with most of the total over time moving to centralized exchanges.

The firm said the sharp rise in money laundering activity in 2021 was not surprising, given the significant growth of both legitimate and illegal crypto activity last year.

Money laundering refers to that process of disguising the origin of illegally obtained money by transferring it to legitimate businesses.

About 17% of the $8.6 billion laundered went to decentralized finance applications, Chainalysis said, referring to the sector dark web link which facilitates crypto-denominated financial transactions outside of traditional banks.

That was up from 2% in 2020.

Mining pools, darknet market darknet markets 2023 2023 high-risk exchanges, and mixers also saw substantial increases in value received from illicit addresses, the report said.

Mixers typically combine potentially identifiable or darknet market list tainted cryptocurrency funds with others, so as to conceal the trail to the fund’s original source.

Wallet addresses associated with theft sent just under half of their stolen funds, or more than $750 million worth of crypto in total, to decentralized finance platforms, according to the Chainalysis report.

Chainalysis also clarified that the $8.6 billion laundered last year represents funds derived from crypto-native crime such as darknet market dark market link sales or ransomware attacks in which profits are in crypto instead of fiat currencies.

“It’s more difficult to measure how much fiat currency derived from off-line crime – traditional drug trafficking, for example – is converted into cryptocurrency to be laundered,” Chainalysis said in the report.

“However, we know anecdotally this is happening.” (Reporting by Gertrude Chavez-Dreyfuss; Editing by Himani Sarkar)

The post Crypto money laundering rises 30% in 2021 -Chainalysis appeared first on The News Max.