Entries Tagged "fraud"

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Cybercriminals Infiltrating E-Mail Networks to Divert Large Customer Payments

There’s a new criminal tactic involving hacking an e-mail account of a company that handles high-value transactions and diverting payments. Here it is in real estate:

The scam generally works like this: Hackers find an opening into a title company’s or realty agent’s email account, track upcoming home purchases scheduled for settlements—the pricier the better—then assume the identity of the title agency person handling the transaction.

Days or sometimes weeks before the settlement, the scammer poses as the title or escrow agent whose email accounts they’ve hijacked and instructs the home buyer to wire the funds needed to close—often hundreds of thousands of dollars, sometimes far more—to the criminals’ own bank accounts, not the title or escrow company’s legitimate accounts. The criminals then withdraw the money and vanish.

Here it is in fine art:

The fraud is relatively simple. Criminals hack into an art dealer’s email account and monitor incoming and outgoing correspondence. When the gallery sends a PDF invoice to a client via email following a sale, the conversation is hijacked. Posing as the gallery, hackers send a duplicate, fraudulent invoice from the same gallery email address, with an accompanying message instructing the client to disregard the first invoice and instead wire payment to the account listed in the fraudulent document.

Once money has been transferred to the criminals’ account, the hackers move the money to avoid detection and then disappear. The same technique is used to intercept payments made by galleries to their artists and others. Because the hackers gain access to the gallery’s email contacts, the scam can spread quickly, with fraudulent emails appearing to come from known sources.

I’m sure it’s happening in other industries as well, probably even with business-to-business commerce.

EDITED TO ADD (11/14): Brian Krebs wrote about this in 2014.

Posted on November 7, 2017 at 6:37 AM • View Comments

Fraud Detection in Pokémon Go

I play Pokémon Go. (There, I’ve admitted it.) One of the interesting aspects of the game I’ve been watching is how the game’s publisher, Niantic, deals with cheaters.

There are three basic types of cheating in Pokémon Go. The first is botting, where a computer plays the game instead of a person. The second is spoofing, which is faking GPS to convince the game that you’re somewhere you’re not. These two cheats are often used together—and you see the results in the many high-level accounts for sale on the Internet. The third type of cheating is the use of third-party apps like trackers to get extra information about the game.

None of this would matter if everyone played independently. The only reason any player cares about whether other players are cheating is that there is a group aspect of the game: gym battling. Everyone’s enjoyment of that part of the game is affected by cheaters who can pretend to be where they’re not, especially if they have lots of powerful Pokémon that they collected effortlessly.

Niantic has been trying to deal with this problem since the game debuted, mostly by banning accounts when it detects cheating. Its initial strategy was basic—algorithmically detecting impossibly fast travel between physical locations or super-human amounts of playing, and then banning those accounts—with limited success. The limiting factor in all of this is false positives. While Niantic wants to stop cheating, it doesn’t want to block or limit any legitimate players. This makes it a very difficult problem, and contributes to the balance in the attacker/defender arms race.

Recently, Niantic implemented two new anti-cheating measures. The first is machine learning to detect cheaters. About this, we know little. The second is to limit the functionality of cheating accounts rather than ban them outright, making it harder for cheaters to know when they’ve been discovered.

“This is may very well be the beginning of Niantic’s machine learning approach to active bot countering,” user Dronpes writes on The Silph Road subreddit. “If the parameters for a shadowban are constantly adjusted server-side, as they can now easily be, then Niantic’s machine learning engineers can train their detection (classification) algorithms in ever-improving, ever more aggressive ways, and botters will constantly be forced to re-evaluate what factors may be triggering the detection.”

One of the expected future features in the game is trading. Creating a market for rare or powerful Pokémon would add a huge additional financial incentive to cheat. Unless Niantic can effectively prevent botting and spoofing, it’s unlikely to implement that feature.

Cheating detection in virtual reality games is going to be a constant problem as these games become more popular, especially if there are ways to monetize the results of cheating. This means that cheater detection will continue to be a critical component of these games’ success. Anything Niantic learns in Pokémon Go will be useful in whatever games come next.

Mystic, level 39—if you must know.

And, yes, I know the game tracks works by tracking your location. I’m all right with that. As I repeatedly say, Internet privacy is all about trade-offs.

Posted on November 3, 2017 at 6:35 AM • View Comments

On the Equifax Data Breach

Last Thursday, Equifax reported a data breach that affects 143 million US customers, about 44% of the population. It’s an extremely serious breach; hackers got access to full names, Social Security numbers, birth dates, addresses, driver’s license numbers—exactly the sort of information criminals can use to impersonate victims to banks, credit card companies, insurance companies, and other businesses vulnerable to fraud.

Many sites posted guides to protecting yourself now that it’s happened. But if you want to prevent this kind of thing from happening again, your only solution is government regulation (as unlikely as that may be at the moment).

The market can’t fix this. Markets work because buyers choose between sellers, and sellers compete for buyers. In case you didn’t notice, you’re not Equifax’s customer. You’re its product.

This happened because your personal information is valuable, and Equifax is in the business of selling it. The company is much more than a credit reporting agency. It’s a data broker. It collects information about all of us, analyzes it all, and then sells those insights.

Its customers are people and organizations who want to buy information: banks looking to lend you money, landlords deciding whether to rent you an apartment, employers deciding whether to hire you, companies trying to figure out whether you’d be a profitable customer—everyone who wants to sell you something, even governments.

It’s not just Equifax. It might be one of the biggest, but there are 2,500 to 4,000 other data brokers that are collecting, storing, and selling information about you—almost all of them companies you’ve never heard of and have no business relationship with.

Surveillance capitalism fuels the Internet, and sometimes it seems that everyone is spying on you. You’re secretly tracked on pretty much every commercial website you visit. Facebook is the largest surveillance organization mankind has created; collecting data on you is its business model. I don’t have a Facebook account, but Facebook still keeps a surprisingly complete dossier on me and my associations—just in case I ever decide to join.

I also don’t have a Gmail account, because I don’t want Google storing my e-mail. But my guess is that it has about half of my e-mail anyway, because so many people I correspond with have accounts. I can’t even avoid it by choosing not to write to gmail.com addresses, because I have no way of knowing if newperson@company.com is hosted at Gmail.

And again, many companies that track us do so in secret, without our knowledge and consent. And most of the time we can’t opt out. Sometimes it’s a company like Equifax that doesn’t answer to us in any way. Sometimes it’s a company like Facebook, which is effectively a monopoly because of its sheer size. And sometimes it’s our cell phone provider. All of them have decided to track us and not compete by offering consumers privacy. Sure, you can tell people not to have an e-mail account or cell phone, but that’s not a realistic option for most people living in 21st-century America.

The companies that collect and sell our data don’t need to keep it secure in order to maintain their market share. They don’t have to answer to us, their products. They know it’s more profitable to save money on security and weather the occasional bout of bad press after a data loss. Yes, we are the ones who suffer when criminals get our data, or when our private information is exposed to the public, but ultimately why should Equifax care?

Yes, it’s a huge black eye for the company—this week. Soon, another company will have suffered a massive data breach and few will remember Equifax’s problem. Does anyone remember last year when Yahoo admitted that it exposed personal information of a billion users in 2013 and another half billion in 2014?

This market failure isn’t unique to data security. There is little improvement in safety and security in any industry until government steps in. Think of food, pharmaceuticals, cars, airplanes, restaurants, workplace conditions, and flame-retardant pajamas.

Market failures like this can only be solved through government intervention. By regulating the security practices of companies that store our data, and fining companies that fail to comply, governments can raise the cost of insecurity high enough that security becomes a cheaper alternative. They can do the same thing by giving individuals affected by these breaches the ability to sue successfully, citing the exposure of personal data itself as a harm.

By all means, take the recommended steps to protect yourself from identity theft in the wake of Equifax’s data breach, but recognize that these steps are only effective on the margins, and that most data security is out of your hands. Perhaps the Federal Trade Commission will get involved, but without evidence of “unfair and deceptive trade practices,” there’s nothing it can do. Perhaps there will be a class-action lawsuit, but because it’s hard to draw a line between any of the many data breaches you’re subjected to and a specific harm, courts are not likely to side with you.

If you don’t like how careless Equifax was with your data, don’t waste your breath complaining to Equifax. Complain to your government.

This essay previously appeared on CNN.com.

EDITED TO ADD: In the early hours of this breach, I did a radio interview where I minimized the ramifications of this. I didn’t know the full extent of the breach, and thought it was just another in an endless string of breaches. I wondered why the press was covering this one and not many of the others. I don’t remember which radio show interviewed me. I kind of hope it didn’t air.

Posted on September 13, 2017 at 12:49 PM • View Comments

New Techniques in Fake Reviews

Research paper: “Automated Crowdturfing Attacks and Defenses in Online Review Systems.”

Abstract: Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.

Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on “usefulness” metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers.

Posted on September 4, 2017 at 7:08 AM • View Comments

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Sidebar photo of Bruce Schneier by Joe MacInnis.