Transcript
Bob Rudis (0:02)
You're listening to the Cyberwire Network powered by N2K.
Navy Advertisement Voice (0:09)
You say you'll never join the Navy, never climb Mount Fuji on a port.
Bob Rudis (0:13)
Visit, or break the sound barrier.
Navy Advertisement Voice (0:15)
Joining the Navy sounds crazy. Saying never actually is. Learn why@navy.com America's Navy forged by the sea.
Dave Bittner (0:30)
Hello everyone and welcome. Welcome to the Cyberwires Research Saturday. I'm Dave Buettner and this is our weekly conversation with researchers and analysts tracking down the threats and vulnerabilities, solving some of the hard problems, and protecting ourselves in our rapidly evolving cyberspace. Thanks for joining us.
Bob Rudis (0:55)
Within the past two years we've had this, what I would call anec data or gut calls that wow, we get this weird spike on some piece of usually enterprise grade edge technology and then like it'll stick in your head that there was a spike and then wait, wait, there's a really interesting or bad or just new set of CVEs that come out like really short period of time later that are either bad or do require patching by folks out there. And like that's happened time and time again.
Dave Bittner (1:24)
That's Bob Rodis, VP of Data Science at Gray Noise. The research we're discussing today is titled Early Warning Signals When Attacker Behavior Precedes New Vulnerabilities.
Bob Rudis (1:42)
So earlier this year we started to say that in our blog posts, not every blog post, but like when there was like a decent uptick either in just normal scanning activity or for a particular cve, we were like, huh, that's interesting that we kept tracking. Six weeks later, roughly between like four to six weeks later, we would see another CVE come out. Sometimes a really bad CVE come out as well. And then we decided to just, okay, we're making a claim and we're hedging it like crazy in the blog posts and like we need to validate it, like we needed to science this thing up. So we took a lot of our data that. So we have an entire new sensor fleet, we have an entire new architecture. So we basically took the entirety of that new architecture, which has been up since about September of last year, and just combed through all of the events that happened there, identified all of the like significant spike outliers. And I won't do math on here, but we put the equations that we used inside the report and took a look at those spikes and then looked at all the related hardware, software, whatever technology that was associated with that spike. So if it was like as an example, if it was a, an avanti CVE that we saw a Spike on or Avanti scanning. We saw a spike on. We grabbed all the CVEs for the related Avanti gear that that might have been really related to. And then we just looked to see when was the published date of the CVEs after that. And we had a couple hundred spike events across like about six or eight technologies. Six really well defined, eight, two more loosely defined that were like, hey, there's a pattern here. Between four to six weeks, you're looking at a new CVE coming out. And we're like, this is, this is useful for us to gauge and to look out for. But we also wanted to make a report about it because it's my having been on the defender side and not on the vendor side for much. I've been on the defender side a lot longer. It's any leg up you can get on knowing when you might have to prepare your defenses for an attacker is, is just great because it's really expensive to like set up extra logging or do a bunch of stuff like that. So we wanted to, you know, show some documented evidence, show some correlation, because we're not, we're not, we don't say causation in the report. Cause it would take a lot more data and a lot more time and a lot more evidence to say real causation. And then after that it's like, like here, like do with this what you will, but we're going to try to put this as a thing that we can put in our product to tell you when there are spikes for a particular technology. But anybody out there with your own logs, you can do the same thing. Because like most everybody will see similar activity that we're seeing for some of the opportunistic scanning. And, and if you have the wherewithal, the bandwidth, the team resources to follow what we did, you could begin to do this predictive stuff too and maybe buy some time with your, with your budget or with your team to prepare your defenses for what might be coming down the pike.
![The CVE countdown clock. [Research Saturday] - CyberWire Daily cover](/_next/image?url=https%3A%2F%2Fmegaphone.imgix.net%2Fpodcasts%2F9b1fdf64-79ea-11f0-9309-6bbd12a09408%2Fimage%2F95b72a93c2ffaf8ff900d662a9bd3735.png%3Fixlib%3Drails-4.3.1%26max-w%3D3000%26max-h%3D3000%26fit%3Dcrop%26auto%3Dformat%2Ccompress&w=1920&q=75)