Guest Perspective: The Ripple Effect of Identity Theft

By Ryan Wilk, director of Customer Success, NuData Security

The Ripple Effect of Identity Theft As a society, we hear about data breaches all the time, but we rarely hear about what happens to the stolen data afterwards. We may not think much of losing one username and password combination, or having to cancel a credit card, but each piece of data doesn’t just disappear. It gets collected and combined into the tool of choice for today’s fraudsters—one that’s so difficult to overcome that we’ve had to rebuild how we do Internet security. 

Data privacy is dead. Since 2005, more than 675 million data records have been involved in data breaches in the U.S. alone, according to the Identity Theft Resource Center . Retailers , healthcare providers , universities and even the U.S. government have experienced massive network security breaches contributing to that number. These records include incredibly personal data such as a person’s Social Security number, name, address, phone number, credit card number, name of local bank branch and so on. Data thieves sell this information to aggregators, who cross-reference and compile full identities – called “fullz” on the data black market, which I will come back to later. This increases the value and usefulness of the stolen data, which may have been gathered from multiple data breaches.

With this level of information, fraudsters can create new bank accounts or take out loans under an actual person’s name. These actions cannot be traced back to the fraudster and can cause problems for the fraud victim for years down the road. In a recent New York Times article , a reporter details how a recent healthcare data breach exposed his child to identity theft that could hinder her for the rest of her life, because her Social Security number was stolen.

Bad News Travels Fast

A recent report found that it took just 12 days for the account information of 1,500 “employees” to travel from California to 22 countries and five continents. In that time, it was viewed over 200 times and clicked on over 1,100 times. Fortunately, in this case, these accounts were set up for fake employees and then intentionally “breached” in order to determine the speed at which compromised data travels.  This is especially disturbing when you consider it takes an average of 200 days for most corporations to detect a breach has taken place.

The experiment didn’t just show how quickly stolen information gets circulated. It determined that the false information was being tested for validity too. Had the fake data actually been real accounts, fraud attempts would already be underway.

It’s the ripple effect.  Small data breaches look on the surface to be minor losses of data but they expand out across the digital waters faster than ever before, converging into a wave of personal information so detailed that undoing the damage is next to impossible.

The Rise of Account Takeover (ATO)

What can you do with all of that stolen information? Depends on how much of it is amassed. There is a hierarchy of value on the dark web for stolen data. Stolen credit cards can cost mere cents and are labor-intensive and low return for fraudsters. It takes many attempts for a fraud scheme to work as cards are tested and cycled through. With so many data breaches last year, credit card numbers flooded the black market, lowering their value.

“Fullz” is a slang term used by credit-card hackers and data resellers meaning full packages of individuals’ identifying information. Fullz sell for $5 apiece, but require a more in-depth and risky scam to be fully utilized. Working user accounts with a payment method attached, an easy-grab scam with lucrative results, go for a mere $27 each and can translate into hundreds to thousands of dollars in stolen money and merchandise. As a result, account takeover has more than doubled year-over-year.

In account takeovers, fraudsters attempt to hijack valid user accounts instead of creating new accounts with stolen credit cards. ATOs can be automated, including scripted attacks, or can be done with small teams of human operators posing as account holders. Helping out the scammers are middlemen who play a key role in testing the login credentials before they are used again to commit actual fraud.

Behavioral analysis shows that there are, on average, three high-risk logins for every high-risk checkout. The first login is to verify if the account works. The second time is to gain intelligence and third time is when the fraudster attempts to commit actual fraud. The transaction is no longer the point of focus for fraud – it is the login. This shift creates an imperative to look at the login and account creation rather than the transaction in order to stop fraud before it happens.

In a sea of available data, account takeover pirates have their pick of digital credentials. Organizations must not only secure their own data but also be ever vigilant against people using stolen data on their websites as well.

By protecting the login pages of your sites, you cut fraudsters off at the source. You stop them from being able to take control of the account in the first place.

Protecting The Source

Most merchants look for a username and password match. Some use device ID or check for password resets. But the newer, more sophisticated criminals are skilled at bypassing these mechanisms. And as we’ve seen, full packages of user information—full identities—are prevalent and cheap.

If you are not confident that you can separate account testers and fraudsters from legitimate users, then the real question you need to ask yourself is, “Do I understand my user in enough detail?”

Rather than a simple checklist, behavioral analytics focuses on observed characteristics of who the user is, not just who they tell you they are. User behavior analytics are aimed at observing and understanding how the user behaves, in an effort to answer bigger questions, such as:

  • How did the user behave previously when they logged in? Are they behaving the same now?
  • When the user is inputting data, is it similar to how they’ve interacted on the same device before, or is it completely different?
  • Is their behavior repeated? Repeated behavior can tell us a lot. If the behavior is the same every time they visit, perhaps we can say it’s a good user, acting the same as always. But if it’s the same behavior that 1,000 users are all repeating, it could indicate that this behavior is part of a crime ring that could be a distributed, low velocity attack – the kind of attack that exposes you to massive amounts of loss.

Breaches, large and small, continue nearly daily. While they are happening often enough that we seem to be becoming desensitized to them, the mountains of legitimate information available to fraudsters continues to expand, putting accounts everywhere at risk. Observing user behavior in detail enables the best chance of beating fraud the fraud that results.

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