Marty also generously provided the slides for his talk.

Some key points I took away:

Algorithms can’t cope with targeted or unknown attacks – monitoring needed

Today’s attacks are rarely massive or brute force, but targeted, sophisticated, more often nation state sponsored, and low and slow (this is particularly important as it means you can’t look for typical spikes, which are a sign a mass event – you have to look at long term issues).

Automated tools of today find known threats and work with predefined patterns – they don’t find unknown attacks (0 days) and the more “heuristic” tools produce lots of false positives (i.e. increase the workload for analysts instead of reducing it)

According to Gartner automatic defense systems (prevention) will become entirely useless from in 2020. Instead, you have to monitor and watch out for malicious behaviour (human eyes!), it won’t be solved automatically.

Some figures for current data amounts in a typical security monitoring setup:

So, if everything works out nicely, you still end up with 1000 (highly aggregated/abstracted) alerts that you have to investigate to find the one incident.

Some security data properties:

Challenges with data mining methods

• Anomaly detection – but how to define “normal”?
• Association rules – but data is sparse, there’s little continuity in web traffic
• Clustering – no good algorithms available (for categorical data, such as user names, IP addresses)
• Classification – data is not consistent (e.g. machine names may change over time)
• Summarization – disrespect “low and slow” values, which are important

How can visualization help?

1. make algorithms at work transparent to the user
2. empower human eyes for understanding, validation, exploration

• because they bring
• supreme pattern recognition
• memory for contexts
• intuition!
• predictive capabilities

This is of course a to-do list for our work!

The need for more research

What is the optimal visualization?

– it depends very much on data at hand and your objectives. But there’s also very few research on that and I’m missing that, actually. E.g. what’s a good visualization for firewall data?

And he even shares one of our core problems, the lack of realistic test data:

That’s hard. VAST has some good sets or you can look for cooperations with companies.

As a couple of powerful viz and analysis libraries are available for Python (such as PANDAS), this is (almost) ideal for recording an analysts way to a result.

Ideas for improvement:

1. make it even more interactive/auto-updating so that changes in one place (“cell”) show up in other places at once (maybe even work with realtime sources?) – maybe towards frameworks like puredata/MAX: this would help explore various parameters for the analysis functions.
2. Think about some auto-recording functions so that documentation becomes easier and the “author” has to think less about it. This might be especially possible in the narrow context of network security analysis where certain procedures are standardized or very common.

See how it works, e.g. with PCAPS (German)

Thanks to Genua who shared their internal training so well recorded and so generously!