In my last post, I presented how to create a YAF application label signature rule that corresponds to a text-based Snort-type rule. In this post, I discuss methods for using Analysis Pipeline to provide context to those signatures.
The context for signatures can take many forms. Some context can be derived from the individual flows that match the signatures. This information is easy to obtain from either SiLK or another traffic analysis tool--just look at the traffic that matched the signature. Analysis Pipeline lets you easily do more. I will discuss three simple options, but Analysis Pipeline can be used for more complex analyses.
Hi all, this is Jonathan Spring with my colleagues Leigh Metcalf and Rhiannon Weaver. We've been studying the dynamics of the Internet blacklist ecosystem for a few years now and the 2015 Verizon Data Breach Investigations Report has corroborated our general results. We get a lot of questions about which list is which and if we can recommend a list. We won't reveal which is which generally, but in this blog post we'll make a small exception (with permission of course) in a case study to update the results.
Ever want to use a Snort-like rule with SiLK or Analysis Pipeline to find text within packets? Timur Snoke and I were recently discussing how we could do this and realized that while neither SiLK nor Analysis Pipeline themselves do packet inspection, YAF can be used to create an application label that can be used in analyses in both SiLK and Pipeline (field 29, application). This post outlines the steps required and provides an example.
Hi. This is Angela Horneman of the SEI's Situational Awareness team. I've generated service specific network flows to use as baseline examples for network analysis and am sharing them since others may find them helpful.
We have been looking at implementing Network Profiling in Analysis Pipeline to automatically generate lists of active servers and to alert when new IPs start acting as servers. As part of this initiative, we started looking at alternatives to using flags in the identification process, since not all collection methods capture TCP flag data. In this process, I looked for example network flows for verified services.
Recently, SuperFish and PrivDog have received some attention because of the risks that they both introduced to customers because of implementation flaws. Looking closer into these types of applications with my trusty CERT Tapioca VM at hand, I've come to realize a few things.
In this blog post, I will explain
The capabilities of SSL and TLS are not well understood by many.
SSL inspection is much more widespread than I suspected.
Many applications that perform SSL inspection have flaws that put users at increased risk.
Even if SSL inspection were performed at least as well as the browsers do, the risk introduced to users is not zero.
Hi folks, Allen Householder here. In my previous post, I introduced our recent work in surveying vulnerability discovery for emerging networked systems (ENS). In this post, I continue with our findings from this effort and look at the differences between ENS and traditional computing in the context of vulnerability discovery, analysis, and disclosure.
Hello, this is Kate Meeuf of the SEI's Situational Awareness team. I'm pleased to announce the publication of the new technical report, Regional Use of Social Networking Tools, which explores regional preferences for social networking tools.
The twelfth practice described in the newly released Common Sense Guide to Mitigating Insider Threats is Practice 12: Deploy solutions for monitoring employee actions and correlating information from multiple data sources. In this post, I discuss this newer practice that involves collecting, managing, and analyzing data from multiple sources that offers insights into insider activity that can lead to cybersecurity incidents.