The Motherboard Guide To Not Getting Hacked is an excellent adjunct to existing guides (I like EFF's Surveillance Self-Defense and The Cryptoparty Handbook) to defending yourself against criminals, stalkers, cops, and other potential intruders into your digital life.
Motherboard's guide distills the practices of its extremely high-caliber staff of security-oriented journalists, including Lorenzo Franceschi-Bicchierai, Joseph Cox, Sarah Jeong, and Jason Koebler -- a significant pedigree indeed.
It's a very accessible guide for laypeople with sensible, easy-to-follow advice. It probably won't help you if you're being targeted by highly resourced state actors, but it might just save you from having your identity stolen by randos who're downloading huge troves of hacked passwords, or vengeful, unhinged business or romantic rivals.
Probably the most important and basic thing you can do to protect yourself is to update the software you use to its newest version. That means using an updated version of whatever operating system you're using, and updating all your apps and software. It also means updating the firmware on your router, connected devices, and any other gadgets you use that can connect to the internet.
Bear in mind that, on your computer, you don't necessarily have to use the latest iteration of an operating system. In some cases, even slightly older versions of operating systems get security updates. (Unfortunately, this is no longer the case with Windows XP—stop using it!) What's most important is that your OS is still receiving security updates, and that you're applying them.
So if you come away with one lesson from this guide is: update, update, update, or patch, patch, patch.
The Motherboard Guide To Not Getting Hacked
Berlin-based security researcher Sébastien Kaul discovered that Voxox (formerly Telcentris) -- a giant, San Diego-based SMS gateway company -- had left millions of SMSes exposed on an Amazon cloud server, with an easily queried search front end that would allow attackers to watch as SMSes with one-time login codes streamed through the service.
Researchers at NYU and U Michigan have published a paper explaining how they used a pair of machine-learning systems to develop a "universal fingerprint" that can fool the lowest-security fingerprint sensors 76% of the time (it is less effective against higher-security sensors).
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