According to Gartner, the number of world-wide Internet connected devices will grow to 11.4 billion by 2018. It’s a phenomenal trend that will continue to spread until human and machine connectivity becomes ubiquitous and unavoidably present. Of course, anything that develops this rapidly will bring a lot of growing pains, and the IoT is no exception. Security hazards are one of the largest concerns. Security has received very little, if any attention. We must remain vigilant.
Quelle: Fraud and the Internet of Things
If you’ve been hurt by online fraud, you know firsthand how frustrating and damaging it can be to a business. And, unfortunately, you’re not alone. Merchants lose an estimated $3.5B in online revenue to fraud annually. Fraud can take the form of chargebacks, fake account sign ups, stolen credit cards, identity theft, and more.
At Sift Science, we analyze a lot of data. We distill fraud signals in real-time from terabytes of data and more than a billion global events per month. Previously, we discovered that the U.S. has more fraud than Nigeria and solved the mystery of Doral, FL. At our “Cats N’ Hacks” Hackathon last week, I decided to put some of our fraud signals to the test. Working with our Machine Learning Engineer, Keren Gu, we discovered some interesting fraud patterns.
Internet fraud is one of the most common motivators of cybercrime. Millions of dollars are stolen every year from victims who are tricked into initiating wire transfer payments through social engineering tactics and computer breaches. This is typically accomplished using one of three methods: business email compromise (BEC), email account compromise (EAC), and spoofing.All three of these methods can be attempted against any organization that relies on email for communication. Being a smaller