Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Vulnerabilities / Threats

4/20/2018
08:00 PM
Connect Directly
Twitter
LinkedIn
Google+
RSS
E-Mail
50%
50%

Cybercrime Economy Generates $1.5 Trillion a Year

Threat actors generate, launder, spend, and reinvest more than $1.5 trillion in illicit funds, according to a new study on cybercrime's 'web of profit.'

RSA CONFERENCE 2018 – San Francisco – If cybercrime was a country, it would have the 13th highest GDP in the world. Attackers generate $1.5 trillion in annual profit, which is about equal to the GDP of Russia, according to a new study on the interconnected economy of cybercrime.

"Into the Web of Profit," among the first studies to explore the intricacies of revenue and profit in the world of cybercrime, was conducted by Dr. Michael McGuire, senior lecturer in Criminology at England's University of Surrey. Over nine months of study, he learned how the "economy" of cybercrime sustains itself and overlaps with the legitimate economy.

This wasn't the original intent behind the Bromium-sponsored study, which began with the idea of learning where cybercriminals spend their money. "It turned into a huge piece of research, which looks at the whole of how money flows around the cybercrime system," says McGuire. The report pieces together conversations with global organizations, security workers who have infiltrated the Dark Web, international police forces, and of course, the criminals themselves.

His study indicates a rise in "platform criminality" similar to the platform capitalism model in which data is the commodity, used by organizations including Amazon and Facebook. This platform turns malware into a product, simplifies purchase of illicit tools and services, and enables broader criminal activities including drug production, human trafficking, and terrorism.

More than 620 new synthetic drug types have appeared on the market since 2005, McGuire says. Many are created in China or India, purchased online, and sent to Europe in bulk. Evidence shows groups earning revenue from cybercrime are also involved in drug production, he found. The takedown of Dark Web online market Alphabay led to the discovery of listings for illegal drugs, toxic chemicals, malware, and stolen and fraudulent data.

The $1.5 trillion that cybercriminals generate each year includes $860 billion in illicit online markets, $500B in theft of trade secrets and intellectual property, $160B in data trading, $1.6B in crimeware-as-a-service, and $1B in ransomware. Evidence indicates cybercrime often generates more revenue than legitimate companies: large multi-national operations can earn more than $1B; smaller ones typically make between $30k-$50K.

It's time to move behind the idea that cybercrime is like a business. "It's much, much more than that," he says. "It's like an economy which mirrors the legitimate economy. Increasingly, what we're seeing is the legitimate economy feeding off the cybercrime economy."

Blurring the Legal Lines

The interdependence between the legitimate and illegitimate economies is driving the "web of profit" fueling cybercrime, McGuire says. Criminal organizations take data and competitive advantages from real companies and luse them to accomplish their goals. Part of the problem is, many of these legitimate organizations don't know their role in furthering cybercrime.

Companies like Facebook and Uber are rich with data, making them a prime target for attackers seeking user information and intellectual property. They give hackers a platform to sell illicit goods and services, and set up fake shops to launder money or connect buyers and sellers. This makes massive companies facilitators in a criminally driven economy.

The owners of cybercrime platforms are the biggest earners, McGuire found. Each hacker might only make $30K per year; however, managers can earn up to $2M per job with as few as 50 stolen credit cards. They aren't committing crime but they are selling it, and their criminal platforms have evolved to offer services, descriptions, and technical support for their buyers.

McGuire shares some of the numbers behind these earnings. A zero-day Adobe exploit, for example, can sell for up to $30K while a zero-day iOS exploit costs $250K. Malware exploit kits cost about $200-600 per exploit; a blackhole exploit kit costs $700 to lease for a month or $1,500 for a full year. Custom spyware costs $200, an SMS-spoofing service runs $20 per month, and a "hacker for hire" will charge about $200 for a minor hack.

Much of the money is reinvested in new criminal ventures. Criminals put about 20% of their revenues into additional crime, indicating up to $300B is used to drive illegal activity.

Related Content:

Kelly Sheridan is the Staff Editor at Dark Reading, where she focuses on cybersecurity news and analysis. She is a business technology journalist who previously reported for InformationWeek, where she covered Microsoft, and Insurance & Technology, where she covered financial ... View Full Bio
 

Recommended Reading:

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...