Russ

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Reactive Malicious Domain Detection (ENTRADA)

One interesting trend of the last year or two is the rising use of data analytics and ANI (Artificial Narrow Intelligence) in solving network engineering problems. Several ideas (and/or solutions) were presented this year at the IETF meeting in Seoul; this post takes a look at one of these. To lay the groundwork, botnets are often controlled through a set of domain names registered just for this purpose. In the same way, domain names are often registered just to provide a base for sending bulk mail (SPAM), phishing attacks, etc. It might be nice for registrars to make some attempt to remove such domains abused for malicious activities, but it’s difficult to know what “normal” activity might look like, or for the registrar to even track the usage of a particular domain to detect malicious activity. One of the papers presented in the Software Defined Network Research Group (SDNRG) addresses this problem directly.

The first problem is actually collecting enough information to analyze in a useful way. DNS servers, even top level domain (TLD) servers collect a huge amount of data—much more than most engineers might suspect. In fact, the DNS system is one of those vast sources of information Continue reading

BGP Tools for the DFZ (2)

In the last post in this series, I looked at the whois database to make certain the registration information for a particular domain name is correct. Now it’s time to dig a little deeper into the DFZ to see what we can find. To put this series in the widest context possible, we will begin by assuming we don’t actually know the Autonomous System number associated with the domain name we’re looking for—which means we will need to somehow find out which AS number belongs to the organization who’s routes we are trying to understand better. The best place to start in our quest for an AS number that matches a domain name is peeringdb. The front page of peeringdb looks like this—

peering-db-01

As the front page says, peeringdb primarily exists to facilitate peering among providers. Assume you find you are a large college, and you find you have a lot of traffic heading to LinkedIn—that, in fact, this traffic is consuming a large amount of your transit traffic through your upstream provider. You would really like to offload this traffic in some way directly to LinkedIn, so you can stop paying the transit costs to this particular network. But Continue reading

On the ‘Web: Fibbing and SDN

The last post on the topic of SDNs discussed BGP as a southbound interface to control policy. This form of SDN was once common in hyperscale data centers (though not as common as it once was). In our pursuit of out of the way (and hence interestingly different) forms of SDNs (hopefully this series will help you understand the scope and meaning of the concept of SDNs by examining both common and uncommon cases), it’s time to look at another unusual form of policy injection—Fibbing. In fibbing, a centralized controller engineers traffic flow in a link state network by interacting with the control plane directly, rather than interacting with the forwarding plane or the RIB. —ECI

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