#y-axis scam

[mightyflamethrower]

"DNA-LEVEL" STATISTICAL PROOF: "Smartmatic" Vote-Counting System Was Manipulated in PA and GA to Overturn Trump's Victory

The charts below are derived from The New York Times' real-time election feeds (e.g., here). They show "DNA-level" evidence of vote fraud that was systematically used to overcome massive Trump leads with "vote flips" to Biden.

The twin charts below depict the shifts in votes starting on election day. The X-axis is the date/time and the Y-axis represents the change in votes (positive values denote shifts for Trump, negative values represent shifts for Biden, in hundreds).

Notice the similarities in PA and GA? How the right sides of the graph show virtually no movement for Trump; and very predictable vote movements to Biden. How predictable?

You have to see the data to really understand the magnitude of the scam.

Below are excerpts of spreadsheets that show what was happening on the right side of each chart. Vote flips in the same-sized bundles (6,000 in PA and 4,800 in GA) were injected into the system to overcome Trump's lead in both states. You can click either image above to see all of the data.

The highlighted cells show where the vote counts -- stunningly obvious in retrospect -- were manipulated to benefit Biden.

Note the vote flips, represented by the highlighted cells, that occurred in both PA and GA. In PA, late vote flips in bundles of around 6,000 were clear anomalies to slowly overcome Trump's lead. In GA, the bundles were in 4,800 vote swaps.

Again, these are just excerpts. You can see the workbooks for yourself here: just click for Pennsylvania and Georgia.

Scroll down until you start hitting the highlighted cells.

Sorry, Democrats: this is what we call DNA-level statistical proof of fraud.

And there's a lot more where this came from. These are just the excerpts.

p.s., can someone who knows Sidney Powell or Joe DiGenova get this info to them?

Hat tip: BadBlue Uncensored News.

[mostlysignssomeportents]

Wework founder raises $70m for blockchain-based, "voluntary” carbon credits

Even crypto’s biggest boosters admit that the industry has a scam problem. This is especially hard to deny now that hundred of billions of (real) dollars’ worth of (fake) “stablecoins” have gone up in smoke.

Crypto apologists will tell you that there are scams everywhere, and they’re not wrong. The productive economy has been systematic dismantled and replaced with financialized grifts: private equity, REITs, SPACs, MLMs, ad-fraud, and monopoly. That means the only way to save for retirement, health emergencies or your kid’s college education is to roll the dice in a crooked casino where the house always wins:

https://pluralistic.net/2022/05/23/you-got-spacced/#the-house-always-wins

But even at this moment of peak scam, crypto stands out as especially scammy. There are some structural factors behind that. For one thing, crypto is complex — in fact, it’s doubly complex, because things like smart contracts can only be truly understood if you can read source-code and a prospectus, and most people understand neither.

Complexity is a fraudster’s best friend. Any time someone adds complexity to a proposition bet (“I’ll pay you X if Y happens”), you should assume that the complexity exists solely to obscure the true odds and rope you into a sucker’s wager:

https://pluralistic.net/2022/05/04/house-always-wins/#are-you-on-drugs

Then there’s pseudonymity and anonymity. Anonymity is key to the right to privacy, but when you combine anonymity with finance — not the right to speak anonymously, but the right to run an investment fund anonymously — you’re rolling out the red carpet for serial scammers, who can run a scam, get caught, change names, and run it again, incorporating the lessons they learned.

Go through the Web3 Is Going Great archive for scams and you’ll see that crypto implosions are rarely one-offs. Typically, the fraudsters who steal millions from crypto gamblers are repeat offenders who’ve refined their grifts through a series of crimes that they were able to outrun by assuming new identities:

https://web3isgoinggreat.com/?theme=hack

When you can’t understand the nature of a financial product, and you can’t know for sure who is offering it, you need some other rule-of-thumb for determining whether you should gamble your savings on it. The big one here is “transitive trust”: I trust this person, and this person says the new coin or product is great, so I’ll trust that, too.

Maybe you trust Larry David, or Matt Damon, or Madonna, or Reese Witherspoon, or Gwyneth Paltrow, or Spike Lee, or Bill Self, or Paris Hilton, or Tom Brady, or Mila Kunis, or Aaron Rodgers, or Stephen Curry, or Marc Cuban, or Shaquille O’Neal, or Serena Williams, or Justin Bieber, or Eminem, or Jimmy Fallon, or Logan Paul, or Snoop Dogg, or…

https://www.nytimes.com/2022/05/17/business/media/crypto-gwyneth-paltrow-matt-damon-reese-witherspoon.html-crypto-disney-adds-subscribers-the-wide-shot

Of course, it’s easy to explain why you might not trust celebrities to give you financial advice — being good at acting or sports or music doesn’t mean you’re good at finance (think of all those stories of celebs who die in poverty after squandering their millions).

But it’s not just celebs who endorse cryptocurrency and related products. Some of the biggest names behind new crypto offerings are A-list investors whose entire public persona is based on their incredible financial acumen.

These investors — including the all-star A16Z, formerly Andreessen Horowitz — have firmly established that they back cryptos based on whether they can make money and get out, not on whether the company they’re investing in is offering a sound, sustainable product. For A16Z, bets on wildly unstable “stablecoins” or imploding, wildly un-fun “games” like Axie Infinity are sound investments — not because these firms have any future, but because coin offerings are unregulated securities that let investors cash out before the companies collapse.

Even amid this pump-and-dump ethos, one of A16Z’s investment stands out as especially cynical. The firm led a $70m Series A investment in Flowcarbon, a “voluntary carbon market” (VCM) cryptocurrency led by Adam Neumann, the notorious Wework founder whose unethical conduct and misleading statements scuttled a $47b IPO.

https://protos.com/flowcarbon-funding-round-proves-reputation-means-nothing-in-crypto/

There were lots of things wrong with Wework, and Neumann was behind all of them. Protos provides some of Neumann’s greatest hits:

Running a business culture shot through with substance abuse

https://archive.ph/C7xS8

Rampant self-dealing, including rent real-estate to his own company and extracting his investors’ cash by offering himself below-market loans out of the company’s coffers:

https://archive.ph/3T5dN

Filing a personal trademark on the word “We” and then charging his own company a $6m license fee to use it (he eventually refunded this, after public outrage):

https://www.businessinsider.com/wework-ceo-gives-back-millions-from-we-trademark-after-criticism-2019-9

Protos doesn’t mention Neumann’s most relevant misconduct, though: employing a nonstandard valuation scheme that allowed him to falsely claim that his company was worth $47b. At the time, I felt this was obviously fraud, and subsequent events bore out this assessment:

https://qz.com/1685919/wework-ipo-community-adjusted-ebitda-and-other-metrics-to-watch-for/

Despite Neumann’s manifest untrustworthiness and his history of capital-destroying financial shenanigans, A16Z has helped him raise $70m. Remember: if you don’t understand crypto products, and you don’t know much about businesspeople, you are forced to rely on the judgments of celebrity investors like A16Z to guide your financial decisions.

Neumann’s new venture, Flowcarbon, will only be profitable if naive, retail investors also buy into it. Flowcarbon’s main product is a speculative crypto asset called the Goddess Nature Token (GNT). What is a GNT? It is meant to represent “a non-binding, self-created carbon offsets for excess pollution.”

Basically, a carbon offset. Now, carbon offsets are already incredibly scammy. Large firms deal in tens of millions of dollars’ worth of carbon credits that represent — for example — forests whose owners have pledged not to log them. But in many cases, these are forest that would never be logged (because they’re owned by a wilderness trust, say). In other cases, the forests have burned down but the credits for not logging them are still being traded:

https://pluralistic.net/2022/03/18/greshams-carbon-law/#papal-indulgences

Flowcarbon’s goal is to take this market for lemons and make it bigger and faster, to facilitate “price transparency, liquidity and accessibility.” The target customers are DAOs and defi projects, especially ones using planet-destroying proof-of-work systems like Ethereum and Bitcoin. These projects will be able to buy carbon credits based on even shakier promises of carbon offsetting and claim that they are carbon neutral.

As Protos points out, the traditional finance markets for these voluntary offsets have failed spectacularly. Companies like the Chicago Climate Exchange (CCX) tanked and took their investors’ capital with them. The major distinction between Flowcarbon and CCX is being “web3, bro” and being helmed by a man whose funny accounting and self-dealing cost his investors $47b.

A16Z and Flowcarbon insist that this will all be very successful: that there will be a long line of people with sound carbon offsets converting them to Goddess Tokens and listing them on Flowcarbon’s exchange, and an equally long line of environmentally concerned DAOs buying those Goddess Tokens.

Their pitch to you, the person hoping to retire without freezing or starving to death, is that you should buy the Goddess Tokens now. Get on the waiting list, and hold Goddess Tokens against the day that they shoot up in value as Adam Neumann — the $47b failure who nevertheless walked away with $480m of his investors’ money — leads the company to glory.

Even worse, the scam at the core of Flowcarbon is carbon offsets. Flowcarbon’s mission is to allow other businesses to claim to be good for the planet based on dubious carbon offset projects. On one side of Flowcarbon’s market, you have investors being lured into buying dubious tokens. On the other side, you have customers of companies who are lured into buying their products because they believe them to be good for the planet.

“Two-sided markets” are the darling of the tech investor set. From Uber to Amazon, tech investors correctly understand that there are fortunes to be made from Chokepoint Capitalism, where a firm corners a market and extracts rent from buyers and sellers who can’t reach each other otherwise.

https://www.penguinrandomhouse.com/books/710957/chokepoint-capitalism-by-cory-doctorow-and-rebecca-giblin/

With Flowcarbon, we see true innovation in two-sided market design. Usually these markets are only an obviously bad deal for one side of the market. For example, Spotify gives listeners access to a lot of music at a low price, while robbing musicians (long-term, Spotify also rips off listeners, too, of course). But Flowcarbon has proposed a two-sided market that’s a bad deal for both sides.

Seen in that light, Adam Neumann is the perfect leader for this business. Wework was sold as a platform for other businesses to run on. Flowcarbon is a scam that is also a platform for scams — scams that roast the planet and bankrupt desperate retail investors, who naively assume that what’s good for A16Z is good for them.

Image:

Lorie Shaull (modified): https://commons.wikimedia.org/wiki/File:A_man_walks_by_a_burning_building_on_Thursday_morning_after_a_night_of_protests_and_rioting_in_Minneapolis,_Minnesota_(49945327763).jpg

CC BY-SA 2.0: https://creativecommons.org/licenses/by-sa/2.0/deed.en

A16Z (modified): https://a16z.com/2022/05/24/investing-in-flowcarbon/

Fair use: https://www.eff.org/issues/bloggers/legal/liability/IP#2

[Image ID: A burning landscape. In the foreground is a burned out building with a charred Wework sign. Over the doorway is a smoke-damaged banner for Flowcarbon with a picture of the planet Earth.]

[honkster]

January 24th - Dream SMP

I just wanted to watch Techno today.

Other recap blogs have done a superior job at summarizing the TFTSMP and Eggpire lore and I truly recommend going to them for the important stuff.

But if u want to vibe with Ranboo and Techno then here you go.

Little summary (spoilers)

<_><_><_><_><_>

Techno and Ranboo had a more grind-y stream that very quickly devolved into chaos and a panicked rushing through Bastions and Nether Fortresses to reach a very far away Woodland Mansion (20k on both axis) because Tales of The SMP started very soon. They get a few totems, Techno scams Ranboo out of a God Apple, sleepy Philza makes an appearance to save those two fools from having to trudge back home 20k blocks with an ender pearl stasis chamber. It’s great.

<_><_><_><_><_>

Little rules to make it easier for me to timestamp

^_^ after a quote - a joke conversation.

:| after a quote - a serious/lore important conversation.

|_| - banter on top of something plot relevant/Something plot relevant in banter form.

Usually if I write *someone* logs on, that means that there's either a little interaction or something that happens when they log in. If something isn't that important, or doesn't relate to any of the SMP lore/drama, I'll usually just leave it out.

Hope that these help get a better picture of all the drama!

The streams that I chose to take timestamps from:

Technoblade – Techno & Ranboo’s Excellent Adventure [Dream SMP]

RanbooLive – Ranboo & Techno’s Excellent Adventure || DreamSMP

<_><_><_><_><_>

Techno’s stream (Just 15 seconds ahead of Ranboo’s stream)

00:03:10 “Hey guys check out what I found” (Fox!!! :D)

00:06:30 “We’re going to be visiting Orphan’s parents” and scamming them for a map.

00:10:35 Ranboo spotted.

00:14:30 Ranboo and Techno get into a vc.

00:27:20 Ocean Monument Pog.

00:41:10 Phil joins the vc.

00:45:00 Techno starts questioning Ranboo’s “species”. (And has an existential crisis over main characters)

01:04:15 Testing out Ranboo’s abilities on a spawner. (The one in Pogtopia)

01:15:25 Foolish spotted.

01:16:25 Niki spotted.

01:22:15 Testing out Ranboo’s abilities on C A K E.

01:35:30 Phil found the perfect angle to get to the portal and back home!

01:54:15 Nether fortress pog! (Totally not used by Techno to farm wither skulls…)

02:06:20 Another Nether Fortress pog! (And a Bastion Pog later too)

02:37:00 SERVER IS GONE CRABRAVE.

02:40:25 SERVER IS BACK CRABRAVE.

02:43:40 THIRD NETHER FORTRESS POG! (Speedrun music plays menacingly)

03:11:25 FINALLY MANSION POG!!!

03:20:05 Techno wakes up Phil to activate the ender pearl stasis chamber.

Techno and Ranboo sell out a bit more before Techno ends stream.

(My personal favorite bits of this stream is right after the server is back and they’re both in a rush to get to the mansion because Tales of The SMP is starting soon and Ranboo’s a part of it so he has to be there! Du Du Du Du through the Nether and the Mansion. Never thought Techno and Ranboo to be the most wonderfully chaotic introvert duo :D)

[aesthetically0b5essed]

Watching CNN put a graph on the television that is intentionally misleading as it makes a 4% growth in stocks look like the fucking shit mooned. It legit looks like a 500% increase unless you read the Y axis and see it doesn't start at 0. Now tell me again how Bitcoin is a scam while we print money out of thin air and increase inflation and it's great because the government can tell the bank not to give you your money anytime they want.

After the wikileaks scandal they stopped visa and Mastercard and banks from processing donations to wikileaks. Guess what they couldn't stop? What they couldn't censor. CRYPTOCURRENCY. See why they hate it? Fiat is another form of control. Decentralize everything.

[cryptswahili]

Infographic: An Overview of Compromised Bitcoin Exchange Events

The purpose of this infographic is to visualize the size of large cryptocurrency hacks that have occurred in the past as if they all happened today. The hacks included in this infographic extend beyond exchanges, as there were other large entities that experienced cryptocurrency hacks, such as marketplaces like Silk Road 2.0. All hacks in this infographic are displayed as if the price of bitcoin was the same when they occurred, in order to visualize their magnitudes in relation to one another.

The x-axis shows the price of bitcoin at the time of the hack. The y-axis shows the amount lost in the hack (converted to BTC for altcoin hacks). The size of each hack circle was determined by the value of BTC lost using a consistent price, regardless of the actual price at the time.

It is important to note that several of the exchanges (rendered in green) were hacks that did not necessarily involve bitcoin or exclusively involve bitcoin.

Mt. Gox

Hack Dates: June 2011, February 2014

Amount Lost: 790,000+ BTC

In March 2014, Mt. Gox declared bankruptcy due to a series of hacks and thefts that went unreported for over three years, which were later documented by blockchain analyst Kim Nilsson. The final collapse resulted in a crash of Bitcoin in 2014. Below is a summary of all meaningful hacks that occured.

On March 1, 2011, 80,000 BTC were stolen from Mt. Gox’s hot wallet, as thieves were able to make a copy of the wallet.dat file. In May 2011, hackers stole 300,000 BTC temporarily stored on an off-site wallet, which was on an unsecured, publicly accessible network drive. However, shortly after, the thief got nervous and returned the stolen funds with a 1 percent (3,000 BTC) “keeper’s fee.” In June 2011, a hacker was able to get into Jed McCaleb’s administrator account and manipulate prices, temporarily crashing the market. After the ordeal was over, the hacker managed to steal 2,000 BTC.

In September 2011, a hacker was able to get read-write access to Mt. Gox’s database. The hacker created new accounts on the exchange, inflated user balances and was able to withdraw 77,500 BTC, after which they deleted most of the logs containing evidence of such transactions. In October 2011, a bug in Mark Karpeles’ new wallet software caused 2,609 BTC to be sent to an unspendable null key. The largest hack occurred at some point between September and October 2011 when a hacker was able to obtain a copy of Mt. Gox’s wallet.dat file and stole 630,000 BTC.

Bitcoinica

Hack Date: March 1, 2012

Amount Lost: 43,000 BTC and then another 18,457 BTC

Web hosting provider Linode’s servers were hacked, granting access to the bitcoin stored on pioneering exchange Bitcoinica. The incidents ultimately led to the demise of Bitcoinica.

BitFloor

Hack Date: September 2012

Amount Lost: 24,000 BTC

BitFloor was compromised when a hacker was able to access unencrypted backups of the exchange’s wallets and transfer out the coins.

Poloniex

Hack Date: March 4, 2014

Amount Lost: 97 BTC

In March 2014, Poloniex announced that it has been the victim of an attack due to a previously unknown vulnerability in its coding. As a result, the exchange told all of its customers that it would have their account balances reduced by 12.3 percent.

Bitstamp

Hack Date: January 2015

Amount Lost: 19,000 BTC

Hackers were able to access Bitstamp’s hot wallet. As a result of the theft, Bitstamp began to keep 98 percent of its bitcoins in cold storage.

Cryptsy

Hack Date: July 2014

Amount Lost: 13,000 BTC

In early 2016, Cryptsy collapsed following the theft of 13,000 BTC (and 30,000 LTC) from customers’ wallets.

Bitfinex

Hack Date: August 2016

Amount Lost: 120,000 BTC

Attackers were able to exploit a vulnerability in the multisig wallet architecture of Bitfinex and blockchain security company BitGo.

QuadrigaCX

Shutdown: January 15, 2019

Amount Lost: Approximately $190 million in BTC, ETH and CAD (at time of publication)

The co-founder of QuadrigaCX died on December 9, 2018, allegedly as the only one with access to the exchange’s keys. Evolving courtroom proceedings have revealed fund mismanagement and potential fraud on the part of the exchange. This has led to calls for greater oversight of exchange operations.

2018’s Cluster of Mishaps in Asia

A cluster of hacks and mismanagement of funds by exchanges in 2018 occurred as the result of minimal regulation and security precautions. Consequently, some exchanges were forced to close operations entirely while others received fines.

Coincheck (Japan)

Hack Date: January 2018

Amount Lost: 523 million NEM

Coinrail (South Korea)

Hack Date: June 2018

Amount Lost: $40 million in various cryptocurrencies

On July 15, 2018, Coinrail resumed trading and offered the victims two compensation options: a gradual refund through the purchase of stolen cryptocurrency or compensation in Coinrail’s RAIL tokens, which could then be converted into another cryptocurrency at an inner rate.

BitHumb (South Korea)

Hack Date: June 2018

Amount Lost: $30 million in various cryptocurrencies

The successful hack of BitHumb occurred shortly after the exchange updated its security systems following an earlier hack in 2017.

Decentralized Exchanges

Bancor

Hack Date: July 9, 2018

Amount Lost: $23 million (mostly in ETH)

Hackers were able to gain control of a Bancor exchange wallet and transfer out funds.

BitGrail

Hack Date: February 21, 2018

Amount Lost: $170 million in XRB, now NANO

Following this hack, authorities in Florence confiscated all of the cryptocurrency from the Italian exchange BitGrail to secure the claim of affected users, and the Nano Foundation promised to assist in the protection of interests and compensation for losses. Users accused the exchange of having lax security.

MyBitcoin

Hack Date: July 2011

Amount Lost: 78,739 BTC

Little information was released about the MyBitcoin theft, however, many argue that operator Tom Williams ran it as a scam. The theft resulted in the closure of MyBitcoin, which was once a successful Bitcoin company in the cryptocurrency’s early days.

Bitomat.pl

Hack Date: July 27 2011

Amount Lost: Approximately 17,000 BTC

During a server restart, the remote Amazon service that housed Bitomat.pl’s wallet was wiped. No backups were kept and Mt. Gox later bailed Bitomat.pl out. Ultimately, neither exchange customers nor original owners suffered any loss from the incident.

Evolution Darknet Marketplace

Hack Date: March 2015

Amount Lost: Approximately 44,000 BTC

In March 2015, Evolution Marketplace administrators “Kimble” and “Verto” were suspected of unexpectedly shutting down Evolution, a darknet marketplace that appeared after the seizure of Silk Road 2.0, and vanishing from the internet with all user funds.

Silk Road 2.0

Hack Date: February 2014

Amount Lost: Approximately 4,400 BTC

Defcon, an administrator at underground marketplace Silk Road 2.0, noticed that funds held for the escrow service were stolen from a hot wallet in February 2014. “Transaction malleability,” an issue with the Bitcoin protocol at the time that also affected some other services, was blamed for the theft, though many suspect it was an inside job.

This article originally appeared on Bitcoin Magazine.

[Telegram Channel | Original Article ]

[cryptobrief]

The purpose of this infographic is to visualize the size of large cryptocurrency hacks that have occurred in the past as if they all happened today. The hacks included in this infographic extend beyond exchanges, as there were other large entities that experienced cryptocurrency hacks, such as marketplaces like Silk Road 2.0. All hacks in this infographic are displayed as if the price of bitcoin was the same when they occurred, in order to visualize their magnitudes in relation to one another.

The x-axis shows the price of bitcoin at the time of the hack. The y-axis shows the amount lost in the hack (converted to BTC for altcoin hacks). The size of each hack circle was determined by the value of BTC lost using a consistent price, regardless of the actual price at the time.

It is important to note that several of the exchanges (rendered in green) were hacks that did not necessarily involve bitcoin or exclusively involve bitcoin.

Mt. Gox

Hack Dates: June 2011, February 2014

Amount Lost: 790,000+ BTC

In March 2014, Mt. Gox declared bankruptcy due to a series of hacks and thefts that went unreported for over three years, which were later documented by blockchain analyst Kim Nilsson. The final collapse resulted in a crash of Bitcoin in 2014. Below is a summary of all meaningful hacks that occured.

On March 1, 2011, 80,000 BTC were stolen from Mt. Gox’s hot wallet, as thieves were able to make a copy of the wallet.dat file. In May 2011, hackers stole 300,000 BTC temporarily stored on an off-site wallet, which was on an unsecured, publicly accessible network drive. However, shortly after, the thief got nervous and returned the stolen funds with a 1 percent (3,000 BTC) “keeper’s fee.” In June 2011, a hacker was able to get into Jed McCaleb’s administrator account and manipulate prices, temporarily crashing the market. After the ordeal was over, the hacker managed to steal 2,000 BTC.

In September 2011, a hacker was able to get read-write access to Mt. Gox’s database. The hacker created new accounts on the exchange, inflated user balances and was able to withdraw 77,500 BTC, after which they deleted most of the logs containing evidence of such transactions. In October 2011, a bug in Mark Karpeles’ new wallet software caused 2,609 BTC to be sent to an unspendable null key. The largest hack occurred at some point between September and October 2011 when a hacker was able to obtain a copy of Mt. Gox’s wallet.dat file and stole 630,000 BTC.

Bitcoinica

Hack Date: March 1, 2012

Amount Lost: 43,000 BTC and then another 18,457 BTC

Web hosting provider Linode’s servers were hacked, granting access to the bitcoin stored on pioneering exchange Bitcoinica. The incidents ultimately led to the demise of Bitcoinica.

BitFloor

Hack Date: September 2012

Amount Lost: 24,000 BTC

BitFloor was compromised when a hacker was able to access unencrypted backups of the exchange’s wallets and transfer out the coins.

Poloniex

Hack Date: March 4, 2014

Amount Lost: 97 BTC

In March 2014, Poloniex announced that it has been the victim of an attack due to a previously unknown vulnerability in its coding. As a result, the exchange told all of its customers that it would have their account balances reduced by 12.3 percent.

Bitstamp

Hack Date: January 2015

Amount Lost: 19,000 BTC

Hackers were able to access Bitstamp’s hot wallet. As a result of the theft, Bitstamp began to keep 98 percent of its bitcoins in cold storage.

Cryptsy

Hack Date: July 2014

Amount Lost: 13,000 BTC

In early 2016, Cryptsy collapsed following the theft of 13,000 BTC (and 30,000 LTC) from customers’ wallets.

Bitfinex

Hack Date: August 2016

Amount Lost: 120,000 BTC

Attackers were able to exploit a vulnerability in the multisig wallet architecture of Bitfinex and blockchain security company BitGo.

QuadrigaCX

Shutdown: January 15, 2019

Amount Lost: Approximately $190 million in BTC, ETH and CAD (at time of publication)

The co-founder of QuadrigaCX died on December 9, 2018, allegedly as the only one with access to the exchange’s keys. Evolving courtroom proceedings have revealed fund mismanagement and potential fraud on the part of the exchange. This has led to calls for greater oversight of exchange operations.

2018’s Cluster of Mishaps in Asia

A cluster of hacks and mismanagement of funds by exchanges in 2018 occurred as the result of minimal regulation and security precautions. Consequently, some exchanges were forced to close operations entirely while others received fines.

Coincheck (Japan)

Hack Date: January 2018

Amount Lost: 523 million NEM

Coinrail (South Korea)

Hack Date: June 2018

Amount Lost: $40 million in various cryptocurrencies

On July 15, 2018, Coinrail resumed trading and offered the victims two compensation options: a gradual refund through the purchase of stolen cryptocurrency or compensation in Coinrail’s RAIL tokens, which could then be converted into another cryptocurrency at an inner rate.

BitHumb (South Korea)

Hack Date: June 2018

Amount Lost: $30 million in various cryptocurrencies

The successful hack of BitHumb occurred shortly after the exchange updated its security systems following an earlier hack in 2017.

Decentralized Exchanges

Bancor

Hack Date: July 9, 2018

Amount Lost: $23 million (mostly in ETH)

Hackers were able to gain control of a Bancor exchange wallet and transfer out funds.

BitGrail

Hack Date: February 21, 2018

Amount Lost: $170 million in XRB, now NANO

Following this hack, authorities in Florence confiscated all of the cryptocurrency from the Italian exchange BitGrail to secure the claim of affected users, and the Nano Foundation promised to assist in the protection of interests and compensation for losses. Users accused the exchange of having lax security.

MyBitcoin

Hack Date: July 2011

Amount Lost: 78,739 BTC

Little information was released about the MyBitcoin theft, however, many argue that operator Tom Williams ran it as a scam. The theft resulted in the closure of MyBitcoin, which was once a successful Bitcoin company in the cryptocurrency’s early days.

Bitomat.pl

Hack Date: July 27 2011

Amount Lost: Approximately 17,000 BTC

During a server restart, the remote Amazon service that housed Bitomat.pl’s wallet was wiped. No backups were kept and Mt. Gox later bailed Bitomat.pl out. Ultimately, neither exchange customers nor original owners suffered any loss from the incident.

Evolution Darknet Marketplace

Hack Date: March 2015

Amount Lost: Approximately 44,000 BTC

In March 2015, Evolution Marketplace administrators “Kimble” and “Verto” were suspected of unexpectedly shutting down Evolution, a darknet marketplace that appeared after the seizure of Silk Road 2.0, and vanishing from the internet with all user funds.

Silk Road 2.0

Hack Date: February 2014

Amount Lost: Approximately 4,400 BTC

Defcon, an administrator at underground marketplace Silk Road 2.0, noticed that funds held for the escrow service were stolen from a hot wallet in February 2014. “Transaction malleability,” an issue with the Bitcoin protocol at the time that also affected some other services, was blamed for the theft, though many suspect it was an inside job.

This article originally appeared on Bitcoin Magazine.

[chicagocraigslistfraudblog]

Desktop Engraving Machine (Rice Lake) $500

3 Axis 3020T-DJ CNC Engraving Machine-MACH3 Setting Machine Desktop New in box, opened never used. Comes with a package of plastic blank tags. No Phone calls, Text or E-mail only. Once I know your not a scam I will be more then happy to talk with y ... from Craigslist https://chicago.craigslist.org/chc/ele/d/rice-lake-desktop-engraving-machine/6845930349.html Fraud Bloggs made possible by: http://circuitgenie.wix.com/techsupport

[iyarpage]

Data Science for Fraud Detection

What is fraud and why is it interesting for Data Science?

Fraud can be defined as “the crime of getting money by deceiving people” (Cambridge Dictionary); it is as old as humanity: whenever two parties exchange goods or conduct business, there is the potential for one party scamming the other. With an ever-increasing use of the internet for shopping, banking, filing insurance claims etc., these businesses have become targets of fraud in a whole new dimension. Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it.

Traditional approaches to identifying fraud have been rule-based. This means that hard and fast rules for flagging a transaction as fraudulent have to be established manually and in advance. But this system isn’t flexible and inevitably results in an arms race between the seller’s fraud detection system and criminals finding ways to circumnavigate these rules. The modern alternative is to leverage the vast amounts of Big Data that can be collected from online transactions and model it in a way that allows us to flag or predict fraud in future transactions. For this, Data Science and Machine Learning techniques such as Deep Neural Networks (DNNs) are the obvious solution!

Here, I am going to show an example of how Data Science techniques can be used to identify fraud in financial transactions. I will offer some insights into the inner workings of fraud analysis, aimed at non-experts to understand.

Synthetic financial datasets for fraud detection

A synthetic financial dataset for fraud detection is openly accessible via Kaggle. It has been generated from a number of real datasets to resemble standard data from financial operations and contains 6,362,620 transactions over 30 days (see Kaggle for details and more information).

By plotting a few major features, we can already get a sense of the data. The two plots below, for example, show us that fraudulent transactions tend to involve larger sums of money. When we also include the transaction type in the visualization, we find that fraud only occurs with tranfers and cash-out transactions and we can adapt our input features for machine learning accordingly.

Fraudulent transactions tend to involve larger sums of money. This plot shows the distribution of transferred amounts of money (log + 1) in fraudulent (Class = 1) and regular (Class = 0) transactions.

Fraud only occurs with tranfers and cash-out transactions. This plot shows the distribution of transferred amounts of money (log + 1) in different transaction types for fraudulent (Class = 1) and regular (Class = 0) transactions.

Dimensionality reduction

In preparation for machine learning analysis, dimensionality reduction techniques are powerful tools for identifying hidden patterns in high-dimensional datasets. In addition, we can use them to reduce the number of features for machine learning while preserving the most important patterns of the data. Similar approaches use clustering algorithms, like k-means clustering.

The most common dimensionality reduction technique is Principal Component Analysis (PCA). PCA is good at picking up linear relationships between features in the data. The first dimension, also called the first principal component (PC), reflects the majority of variation in our data, the second PC reflects the second-biggest variation and so on. When we plot the first two dimensions against each other in a scatterplot, we see patterns in our data: The more dissimilar two samples in our dataset, the farther apart they will be in a PCA plot. PCA will not be able to deal with more complex patterns, though. For non-linear patterns, we can use t-Distributed Stochastic Neighbor Embedding (t-SNE). In contrast to PCA, t-SNE will not only show sample dissimilarity, it will also account for similarity by clustering similar samples close together in a plot. This might not sound like a major difference, but when we look at the plots below, we can see that it is much easier to identify clusters of fraudulent transactions with t-SNE than with PCA. PCA and t-SNE can both be used with machine learning.

Here, I want to use dimensionality reduction and visualization to perform a sanity check on the labelled training data. Because we can assume that some fraud cases might not have been identified as such (and are therefore mis-labelled), we could now advise to take a closer look at non-fraud samples that cluster with fraud cases.

Dimensionality reduction techniques in fraud analytics. The plots show the first two dimensions of PCA (left) and t-SNE (right) for fraudulent (Class = 1) and regular (Class = 0) transactions.

Which Machine Learning algorithms are suitable for fraud analysis?

Machine learning is a broad field. It encompasses a large collection of algorithms and techniques that are used in classification, regression, clustering or anomaly detection. Two main classes of algorithms, for supervised and unsupervised learning, can be distinguished.  

Supervised learning is used to predict either the values of a response variable (regression tasks) or the labels of a set of pre-defined categories (classification tasks). Supervised learning algorithms learn how to predict unknown samples based on the data of samples with known response variables/labels.

In our fraud detection example, we are technically dealing with a classification task: For each sample (i.e. transaction), the pre-defined label tells us whether it is fraudulent (1) or not (0). However, there are two main problems when using supervised learning algorithms for fraud detection:

Data labelling: In many cases, fraud is difficult to identify. Some cases will be glaringly obvious – these are easy to recognize with rule-based techniques and usually won’t require complex models. Where it becomes interesting are the subtle cases; they are hard to recognize as we don’t usually know what to look for. Here, the power of machine learning comes into play! But because fraud is hard to detect, training data sets from past transactions are probably not classified correctly in many of these subtle cases. This means that the pre-defined labels will be wrong for some of the transactions. If this is the case, supervised machine learning algorithms won’t be able to learn to find these types of fraud in future transactions.

Unbalanced data: An important characteristic of fraud data is that it is highly unbalanced. This means that one class is much more frequent than the other; in our example, less than 1% of all transactions are fraudulent (see figure “Synthetic financial dataset for fraud detection”). Most supervised machine learning classification algorithms are sensitive to unbalance in the predictor classes, and special techniques would have to be used to account for this unbalance.

Synthetic financial dataset for fraud detection. Fraud cases are rare compared to regular transactions; in the simulated example dataset less than 1% of all transactions are fraudulent.

  Unsupervised learning doesn’t require pre-defined labels or response variables; it is used to identify clusters or outliers/anomalies in data sets.

In our fraud example data set we don’t trust the predictor labels to be 100% correct. But we can assume that fraudulent transactions will be sufficiently different from the vast majority of regular transactions, so that unsupervised learning algorithms will flag them as anomalies or outliers.

Anomaly detection with deep learning autoencoders

Neural networks are applied to supervised and unsupervised learning tasks. Autoencoder neural networks are used for anomaly detection in unsupervised learning; they apply backpropagation to learn an approximation to the identity function, where the output values are equal to the input. They do so by minimizing the reconstruction error or loss. Because the reconstruction error is minimized according to the background signal of regular samples, anomalous samples will have a larger reconstruction error.

For modeling, I am using the open-source machine learning software H2O via the “h2o” R package. On the fraud example data set described above, an unsupervised neural network was trained using deep learning autoencoders (Gaussian distribution, quadratic loss, 209 weights/biases, 42,091,943 training samples, mini-batch size 1, 3 hidden layers with [10, 2, 10] nodes). The training set contains only non-fraud samples, so that the autoencoder model will learn the “normal” pattern in the data; test data contains a mix of non-fraud and fraud samples. We need to keep in mind, though, that autoencoder models will be sensitive to outliers in our data in that they might throw off otherwise typical patterns. This trained autoencoder model can now identify anomalies or outlier instances based on the reconstruction mean squared error (MSE): transactions with a high MSE are outliers compared to the global pattern of our data. The figure below shows that the majority of test cases that had been labelled as fraudulent indeed have a higher MSE. We can also see that a few regular cases have a slightly higher MSE; these might contain cases of novel fraud mechanisms that have been missed in previous analyses.

This plot shows reconstruction MSE (y-axis) for every transaction (instance) in the test data set (x-axis); points are colored according to their pre-defined label (fraud = 1, regular = 0).

Pre-training supervised models with autoencoders

Autoencoder models can also be used for pre-training supervised learning models. On an independent training sample, another deep neural network was trained – this time for classification of the response variable “Class” (fraud = 1, regular = 0) using the weights from the autoencoder model for model fitting (2-class classification, Bernoulli distribution, CrossEntropy loss, 154 weights/biases, 111,836,076 training samples, mini-batch size 1, balance_classes = TRUE).

Model performance is evaluated on the same test set that was used for showing the MSE of the autoencoder model above. The plot below shows the predicted versus actual class labels. Because we are dealing with severely unbalanced data, we need to evaluate our model based on the rare class of interest, here fraud (class 1). If we looked at overall model accuracy, a model that never identifies instances as fraud would still achieve a > 99% accuracy. Such a model would not serve our purpose. We are therefore interested in the evaluation parameters “sensitivity” and “precision”: We want to optimize our model so that a high percentage of all fraud cases in the test set is predicted as fraud (sensitivity), and simultaneously a high percentage of all fraud predictions is correct (precision). An optimal outcome from training a supervised neural network for binary classification is shown in the plot below.

Results from training a supervised neural network for binary classification. The plot shows the percentage of correctly classified transactions by comparing actual class labels (x-axis) with predicted labels (color; fraud = 1, regular = 0).

Understanding and trusting machine learning models

Decisions made by machine learning models are inherently difficult – if not impossible – for us to understand. The complexity of some of the most accurate classifiers, like neural networks, is what makes them perform so well. But it also basically makes them a black box. This can be problematic, because executives will be less inclined to trust and act on a decision they don’t understand.

Local Interpretable Model-Agnostic Explanations (LIME) is an attempt to make these complex models at least partly understandable; With LIME, we are able to explain in more concrete terms why, for example, a transaction that was labelled as regular might have been classified as fraudulent. The method has been published in “Why Should I Trust You? Explaining the Predictions of Any Classifier” by Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin from the University of Washington in Seattle. It makes use of the fact that linear models are easy to explain; LIME approximates a complex model function by locally fitting linear models to permutations of the original training set. On each permutation, a linear model is being fit and weights are given so that positive weights support a decision and negative weights contradict them. In sum, this will give an approximation of how much and in which way each feature contributed to a decision made by the model.

Code

A full example with code for training autoencoders and for using LIME can be found on my personal blog:

Autoencoders and anomaly detection with machine learning Part 1 & Part 2

Explaining complex machine learning models with LIME

The post Data Science for Fraud Detection appeared first on codecentric AG Blog.

Data Science for Fraud Detection published first on http://ift.tt/2fA8nUr

[mobilenamic]

Data Science for Fraud Detection

What is fraud and why is it interesting for Data Science?

Fraud can be defined as “the crime of getting money by deceiving people” (Cambridge Dictionary); it is as old as humanity: whenever two parties exchange goods or conduct business there is the potential for one party scamming the other. With an ever increasing use of the internet for shopping, banking, filing insurance claims, etc. these businesses have become targets of fraud in a whole new dimension. Fraud has become a major problem in e-commerce and a lot of resources are being invested to recognize and prevent it.

Traditional approaches to identifying fraud have been rule-based. This means that hard and fast rules for flagging a transaction as fraudulent have to be established manually and in advance. But this system isn’t flexible and inevitably results in an arms-race between the seller’s fraud detection system and criminals finding ways to circumnavigate these rules. The modern alternative is to leverage the vast amounts of Big Data that can be collected from online transactions and model it in a way that allows us to flag or predict fraud in future transactions. For this, Data Science and Machine Learning techniques, like Deep Neural Networks (DNNs), are the obvious solution!

Here, I am going to show an example of how Data Science techniques can be used to identify fraud in financial transactions. I will offer some insights into the inner workings of fraud analysis, aimed at non-experts to understand.

Synthetic financial datasets for fraud detection

A synthetic financial dataset for fraud detection is openly accessible via Kaggle. It has been generated from a number of real datasets to resemble standard data from financial operations and contains 6,362,620 transactions over 30 days (see Kaggle for details and more information).

By plotting a few major features, we can already get a sense of the data. The two plots below, for example, show us that fraudulent transactions tend to involve larger sums of money. When we also include the transaction type in the visualization, we find that fraud only occurs with tranfers and cash-out transactions and we can adapt our input features for machine learning accordingly.

Fraudulent transactions tend to involve larger sums of money. This plot shows the distribution of transferred amounts of money (log + 1) in fraudulent (Class = 1) and regular (Class = 0) transactions.

Fraud only occurs with tranfers and cash-out transactions. This plot shows the distribution of transferred amounts of money (log + 1) in different transaction types for fraudulent (Class = 1) and regular (Class = 0) transactions.

Dimensionality reduction

In preparation for machine learning analysis, dimensionality reduction techniques are powerful tools for identifying hidden patterns in high-dimensional datasets. In addition, we can use them to reduce the number of features for machine learning while preserving the most important patterns of the data. Similar approaches use clustering algorithms, like k-means clustering.

The most common dimensionality reduction technique is Principal Component Analysis (PCA). PCA is good at picking up linear relationship between features in the data. The first dimension, also called the first principal component (PC), reflects the majority of variation in our data, the second PC reflects the second-biggest variation and so on. When we plot the first two dimensions against each other in a scatterplot, we see patterns in our data: the more dissimilar two samples in our dataset, the farther apart they will be in a PCA plot. PCA will not be able to deal with more complex patterns, though. For non-linear patterns, we can use t-Distributed Stochastic Neighbor Embedding (t-SNE). In contrast to PCA, t-SNE will not only show sample dissimilarity, it will also account for similarity by clustering similar samples close together in a plot. This might not sound like a major difference, but when we look at the plots below, we can see that it is much easier to identify clusters of fraudulent transactions with t-SNE than with PCA. PCA and t-SNE can both be used with machine learning.

Here, I want to use dimensionality reduction and visualization to perform a sanity check on the labelled training data. Because we can assume that some fraud cases might not have been identified as such (and are therefore mis-labelled), we could now advise to take a closer look at non-fraud samples that cluster with fraud cases.

Dimensionality reduction techniques in fraud analytics. The plots show the first two dimensions of PCA (left) and t-SNE (right) for fraudulent (Class = 1) and regular (Class = 0) transactions.

Which Machine Learning algorithms are suitable for fraud analysis?

Machine Learning is a broad field. It encompasses a large collection of algorithms and techniques that are used in classification, regression, clustering or anomaly detection. Two main classes of algorithms, for supervised and unsupervised learning, can be distinguished.  

Supervised learning is used to predict either the values of a response variable (regression tasks) or the labels of a set of pre-defined categories (classification tasks). Supervised learning algorithms learn how to predict unknown samples based on the data of samples with known response variables/labels.

In our fraud detection example, we are technically dealing with a classification task: for each sample (i.e. transaction), the pre-defined label tells us whether it is fraudulent (1) or not (0). However, there are two main problems when using supervised learning algorithms for fraud detection:

Data labelling: In many cases, fraud is difficult to identify. Some cases will be glaringly obvious – these are easy to recognize with rule-based techniques and usually won’t require complex models. Where it becomes interesting are the subtle cases; they are hard to recognize as we don’t usually know what to look for. Here, the power of machine learning comes into play! But because fraud is hard to detect, training data sets from past transactions are probably not classified correctly in many of these subtle cases. This means that the pre-defined labels will be wrong for some of the transactions. If this is the case, supervised machine learning algorithms won’t be able to learn to find these types of fraud in future transactions.

Unbalanced data: An important characteristic of fraud data is that it is highly unbalanced. This means that one class is much more frequent than the other; in our example, less than 1% of all transactions are fraudulent (see figure “Synthetic financial dataset for fraud detection”). Most supervised machine learning classification algorithms are sensitive to unbalance in the predictor classes, and special techniques would have to be used to account for this unbalance.

Synthetic financial dataset for fraud detection. Fraud cases are rare compared to regular transactions; in the simulated example dataset less than 1% of all transactions are fraudulent.

  Unsupervised learning doesn’t require pre-defined labels or response variables; it is used to identify clusters or outliers/anomalies in data sets.

In our fraud example data set we don’t trust the predictor labels to be 100% correct. But we can assume that fraudulent transactions will be sufficiently different from the vast majority of regular transactions, so that unsupervised learning algorithms will flag them as anomalies or outliers.

Anomaly detection with deep learning autoencoders

Neural networks are applied to supervised and unsupervised learning tasks. Autoencoder neural networks are used for anomaly detection in unsupervised learning; they apply backpropagation to learn an approximation to the identity function, where the output values are equal to the input. They do so by minimizing the reconstruction error or loss. Because the reconstruction error is minimized according to the background signal of regular samples, anomalous samples will have a larger reconstruction error.

For modeling, I am using the open-source machine learning software H2O via the “h2o” R package. On the fraud example data set described above, an unsupervised neural network was trained using deep learning autoencoders (gaussian distribution, quadratic loss, 209 weights/biases, 42,091,943 training samples, mini-batch size 1, 3 hidden layers with [10, 2, 10] nodes). The training set contains only non-fraud samples, so that the autoencoder model will learn the “normal” pattern in the data; test data contains a mix of non-fraud and fraud samples. We need to keep in mind though, that autoencoder models will be sensitive to outliers in our data in that they might throw off otherwise typical patterns. This trained autoencoder model can now identify anomalies or outlier instances based on the reconstruction mean squared error (MSE): transactions with a high MSE are outliers compared to the global pattern of our data. The figure below shows that the majority of test cases that had been labelled as fraudulent indeed have a higher MSE. We can also see that a few regular cases have a slightly higher MSE; these might contain cases of novel fraud mechanisms that have been missed in previous analyses.

This plot shows reconstruction MSE (y-axis) for every transaction (instance) in the test data set (x-axis); points are colored according to their pre-defined label (fraud = 1, regular = 0).

Pre-training supervised models with autoencoders

Autoencoder models can also be used for pre-training supervised learning models. On an independent training sample, another deep neural network was trained – this time for classification of the response variable “Class” (fraud = 1, regular = 0) using the weights from the autoencoder model for model fitting (2-class classification, Bernoulli distribution, CrossEntropy loss, 154 weights/biases, 111,836,076 training samples, mini-batch size 1, balance_classes = TRUE).

Model performance is evaluated on the same test set that was used for showing the MSE of the autoencoder model above. The plot below shows the predicted versus actual class labels. Because we are dealing with severely unbalanced data, we need to evaluate our model based on the rare class of interest, here fraud (class 1). Would we look at overall model accuracy, a model that never identifies instances as fraud would still achieve a > 99% accuracy. Such a model would not serve our purpose. We are therefore interested in the evaluation parameters “sensitivity” and “precision”: we want to optimize our model so that a high percentage of all fraud cases in the test set is predicted as fraud (sensitivity), and simultaneously a high percentage of all fraud predictions is correct (precision). An optimal outcome from training a supervised neural network for binary classification is shown in the plot below.

Results from training a supervised neural network for binary classification. The plot shows the percentage of correctly classified transactions by comparing actual class labels (x-axis) with predicted labels (color; fraud = 1, regular = 0).

Understanding and trusting machine learning models

Decisions made by machine learning models are inherently difficult – if not impossible – for us to understand. The complexity of some of the most accurate classifiers, like neural networks, is what makes them perform so well. But it also basically makes them a black box. This can be problematic, because executives will be less inclined to trust and act on a decision they don’t understand.

Local Interpretable Model-Agnostic Explanations (LIME) is an attempt to make these complex models at least partly understandable; With LIME, we are able to explain in more concrete terms why, for example, a transaction that was labelled as regular might have been classified as fraudulent. The method has been published in “Why Should I Trust You? Explaining the Predictions of Any Classifier.” by Marco Tulio Ribeiro, Sameer Singh and Carlos Guestrin from the University of Washington in Seattle. It makes use of the fact that linear models are easy to explain; LIME approximates a complex model function by locally fitting linear models to permutations of the original training set. On each permutation, a linear model is being fit and weights are given so that positive weights support a decision and negative weights contradict them. In sum, this will give an approximation of how much and in which way each feature contributed to a decision made by the model.

Code

A full example with code for training autoencoders and for using LIME can be found on my personal blog:

Autoencoders and anomaly detection with machine learning Part 1 & Part 2

Explaining complex machine learning models with LIME

The post Data Science for Fraud Detection appeared first on codecentric AG Blog.

Data Science for Fraud Detection published first on http://ift.tt/2vCN0WJ

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Skilled professionals can migrate to Canada through:

·        Federal Skilled Worker Program (FSWP)

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·        Canada Experience Class (CEC)

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