Why the Colonial Pipeline ransomware attack is a sign of things to come

Ransomware has grown fouler than ever, but it’s also grown up. The practice of using malware to encrypt files on a victim’s devices and then demanding a ransom payment for unlocking them has advanced far beyond its origins as a nuisance for individual users. These days, it’s a massively profitable business that has spawned its own ecosystem of partner and affiliate firms. And as a succession of security experts made clear at the RSA Conference last week, we remain nowhere near developing an equivalent of a vaccine for this online plague. “It’s professionalized more than it’s ever been,” said Raj Samani, chief scientist at McAfee, in an RSA panel . “Criminals are starting to make more money,” said Jen Miller-Osborn, deputy director of threat intelligence at Palo Alto Networks’ Unit 42, in another session . She added that the average ransomware payout now exceeds $300,000, fueled by such tactics as the “double extortion” method of exfiltrating sensitive data from targeted systems and then threatening to post it. That method figured in recent ransomware attacks against Colonial Pipeline and Washington, D.C.’s Metropolitan Police Department . “It’s such a lucrative business now for the criminals, it is going to take a full court press to change that business model,” agreed Michael Daniel, president and CEO of the Cyber Threat Alliance, in that panel. (Just five years ago, the $17,000 ransom reportedly paid by a compromised hospital was a newsworthy figure.) Having this much money sloshing around has given rise to networks of affiliates and brokers. Samani’s colleague John Fokker, head of cyber investigations at McAfee, explained the rise of “ransomware as a service” (“RaaS”), in which you can buy or rent exploit kits or back doors into companies. He showed one ad from an “access broker” that listed a price of $7,500 for compromised Virtual Private Network accounts at an unspecified Canadian firm. The ad vaguely described this target company as a “Consumer Goods (manufacturing, retailing, food etc…)” enterprise with about 9,000 employees and $3 billion in revenue. “The commoditization of these capabilities for the criminals makes it so easy,” said Phil Reiner, CEO of the Institute for Security and Technology, during one of the RSA panels. RSA speakers noted how often ransomware attacks start with exploitations of known, avoidable vulnerabilities. Samani called Microsoft’s Remote Desktop Protocol “the number-one most common entry vector for corporate networks related to ransomware attacks.” Fokker added that companies that use RDP often make this remote-access tool too easy to compromise, joking that RDP also means “really dumb passwords.” The pandemic has helped grease the skids further for ransomware attacks—both by requiring companies to rush into remote work and by making people a little more tempted to respond to COVID-themed phishing lures. As Samani put it, phishing is “still there, still works, people still click on links.” Two other factors make ransomware especially resistant to any suppression attempts. One is cryptocurrency enabling hard-to-trace online funds transfers. Bitcoin and other digital currencies may not be too useful for everyday transactions , but they suit the business of ransomware well Read More …

Today’s AI isn’t prepared for the messiness of reality

What began as a warning label on financial statements has become useful advice for how to think about almost anything: “Past performance is no guarantee of future results.” So why do so many in the AI field insist on believing the opposite? Too many researchers and practitioners remain stuck on the idea that the data they gathered in the past will produce flawless predictions for future data. If the past data are good, then the outcome will also be good in the future. That line of thinking received a major wake-up call recently when an MIT study found that the 10 most-cited data sets were riddled with label errors (in the training dataset, a picture of a dog is labeled as a cat, for example). These data sets form the foundation of how many AI systems are built and tested, so pervasive errors could mean that AI isn’t as advanced as we may think. After all, if AI can’t tell the difference between a mushroom and a spoon, or between the sound of Ariana Grande hitting a high note and a whistle (as the MIT study found and this MIT Tech Review article denotes), then why should we trust it to make decisions about our health or to drive our cars? The knee-jerk response from academia has been to refocus on cleaning up these benchmark data sets Read More …

Today’s AI isn’t prepared for the messiness of reality

What began as a warning label on financial statements has become useful advice for how to think about almost anything: “Past performance is no guarantee of future results.” So why do so many in the AI field insist on believing the opposite? Too many researchers and practitioners remain stuck on the idea that the data they gathered in the past will produce flawless predictions for future data. If the past data are good, then the outcome will also be good in the future. That line of thinking received a major wake-up call recently when an MIT study found that the 10 most-cited data sets were riddled with label errors (in the training dataset, a picture of a dog is labeled as a cat, for example). These data sets form the foundation of how many AI systems are built and tested, so pervasive errors could mean that AI isn’t as advanced as we may think. After all, if AI can’t tell the difference between a mushroom and a spoon, or between the sound of Ariana Grande hitting a high note and a whistle (as the MIT study found and this MIT Tech Review article denotes), then why should we trust it to make decisions about our health or to drive our cars? The knee-jerk response from academia has been to refocus on cleaning up these benchmark data sets. We can continue to obsess over creating clean data for AI to learn from in a sterile environment, or we can put AI in the real world and watch it grow. Currently, AI is like a mouse raised to thrive in a lab: If it’s let loose into a crowded, polluted city, its chances for surviving are pretty slim. Every AI Will Always Be Wrong Because AI started in academia, it suffers from a fundamental problem of that environment, which is the drive to control how things are tested. This, of course, becomes a problem when academia meets the real world, where conditions are anything but controlled. Tellingly, AI’s relative success in an academic setting has begun to work against it as businesses adopt it Read More …

Here’s how to fix cryptocurrency’s energy consumption problem

Just as with mining in the real world, the people who mine cryptocurrency use powerful equipment to increase their chance of finding valuable resources. But all that power comes at a cost: namely, massive energy consumption. However, an alternative method for unearthing digital gold could change all that. It’s called “proof of stake,” and it’s a much more computationally efficient form of cryptocurrency mining. A very brief explanation of crypto mining This section could fill an entire book, but the very basic idea is that cryptocurrencies are decentralized, meaning that there’s no “bank” that holds all the digital coins Read More …

The Gmail-enhancing superpower you didn’t know you needed

For a service that’s all about interacting with other (alleged) humans, Gmail does a curiously poor job of putting people front and center. Sure, the Gmail inbox is all about communication—but have you ever found yourself staring at an email and struggling to remember what you know about the person who sent it or exchanges you’ve had in the past? If you interact with enough mammals over email, it’s bound to happen. And Gmail just doesn’t have particularly powerful tools for providing the on-demand context you need to successfully navigate your way out of that situation. Up until a matter of months ago, in fact, Gmail didn’t have any real form of integration with its companion Google Contacts service. Late last year, Google added a Contacts panel into the website’s sidebar, which was a significant step—but the information in that panel is still pretty limited and lacking. You can see basic contact info for people who emailed you and a list of past emails involving them, and that’s about it. If you want any additional details, you’ll have to stop what you’re doing, click away from the message, and move over to a whole other page to find it. Let me tell you: You can do better. With the right add-on, you can bring detailed, genuinely helpful contact information right into your inbox—so you can see it right alongside messages and gain the context you need to interact intelligently. It’s similar to what you’d get with a customer relationship management (CRM) system, but you don’t have to work in sales to benefit from its presence. It’s an incredibly basic email function, in fact, and once you see how good Gmail can be with it in the mix, you won’t want to go back. Try out one of these three exceptional Gmail contact-enriching tools, and watch your email efficiency soar. Gmail enhancer #1: The custom contacts panel The simplest Gmail contacts enhancement worth considering is an add-on called, rather appropriately, Contacts+ . The service is available in a variety of forms , but the one you’ll want to use is the Chrome browser extension , which brings the most pertinent info directly into the Gmail website. Once you have the extension installed and you’ve signed up with the service, you’ll find a button to activate it in the panel at the right of the Gmail desktop site (if you don’t see that button, the little left-facing arrow in the lower-right corner of the screen will reveal it) Read More …