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IoT From a Network Perspective: Learning to Navigate the New Realms

Let’s play the analogy game. The Internet of Things (IoT) is probably going end up being like… a box of chocolates, because you never do know what you are going to get? a big bowl of spaghetti with a serious lack of meatballs? Whatever it is, the IoT should have network folks worried about security. Of course, there is the problem of IoT devices being attached to random places on the network, exfiltrating personal data back to a cloud server you don’t know anything about. Some of these devices might be rogue, of course, such as Raspberry Pi attached to some random place in the network. Others might be more conventional, such as those new exercise machines the company just brought into the gym that’s sending personal information in the clear to an outside service.

While there is research into how to tell the difference between IoT and “larger” devices, the reality is spoofing, and blurred lines will likely make such classification difficult. What do you do with a virtual machine that looks like a Raspberry Pi running on a corporate laptop for completely legitimate reasons? Or what about the Raspberry Pi-like device that can run a fully operational Windows stack, including “background noise” applications that make it look like a normal compute platform? These problems are, unfortunately, not easy to solve.

To make matters worse, there are no standards by which to judge the security of an IoT device. Even if the device manufacturer—think about the new gym equipment here—has the best intentions towards security, there is almost no way to determine if a particular device is designed and built with good security. The result is that IoT devices are often infected and used as part of a botnet for DDoS, or other, attacks.

What are our options here from a network perspective? The most common answer to this is segmentation—and segmentation is, in fact, a good start on solving the problem of IoT. But we are going to need a lot more than segmentation to avert certain disaster in our networks. Once these devices are segmented off, what do we do with the traffic? Do we just allow it all (“hey, that’s an IoT device, so let it send whatever it wants to… after all, it’s been segmented off the main network anyway”)? Do we try to manage and control what information is being exfiltrated from our networks? Is machine learning going to step in to solve these problems? Can it, really?

To put it another way—the attack surface we’re facing here is huge, and the smallest mistake can have very bad ramifications in individual lives. Take, for instance, the problem of data and IoT devices in abusive relationships. Relationships are dynamic; how is your company going to know when an employee is in an abusive relationship, and thus when certain kinds of access should be shut off? There is so much information here; it seems almost impossible to manage it all.

It looks, to me, like the future is going to be a bit rough and tumble as we learn to navigate this new realm. Vendors will have lots of good ideas (look at Mists’ capabilities in tracking down the location of rogue devices, for instance). Still, in the end, it’s going to be the operational front line that is going to have to figure out how to manage and deploy networks where there is a broad blend of ultimately untrustable IoT devices and more traditional devices.

Now would be the time to start learning about security, privacy, and IoT if you haven’t started already.

By Russ White, Infrastructure Architect at Juniper Networks

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