Cyber Intelligence Tsunami

Published: 2013-07-19
Last Updated: 2013-07-19 09:18:14 UTC
by Stephen Hall (Version: 1)
3 comment(s)

This week fellow handler Chris posted about gathering intelligence from Blog Spam, and the SANS ISC has posted a number of times about Cyber Intelligence as a valuable resource, and as by now you all should know that Russ may have posted on his Blog about CIF, the Collective Intelligence Framework.

CIF, out of the box links with only a little bit of configuration with a number of automated ingested intelligence feeds, including some from the SANS ISC.

So, once you have all this open source intelligence gathered, we know that one of the powers of CIF is that you can produce SNORT rules, IPTABLES rules etc, but that is only the start.

MITRE has this year released definitions for STIX, TAXII and CYBOX to aid in this space, to allow analysts to describe and transfer cyber intelligence from place to place, from peer organisation to peer organisation, or indeed from cyber intelligence hub to their members. There are other ways this has been defined, and IODEF is one of those.

So, what is the next step, assuming you have implemented some sort of automated intelligence gathering operation, you will have a database or similar now full of actionable information. How do you apply that to your organisations, how do you enrich that information to make it true actionable intellgence.

The next step is to bolt into (or implement if you have not already) the automation you have in place within your organisation to search your security logs for potential hits  for these indicators.

Examples here can include utilising the SPLUNK! API to automate the searches for C2 indicators, or other searches across your logs using regex of the data you have collected. A good open source example of this is using MalwareSigs to provide regular jobs you can run to search for badness.

So, once your searches have found hits, what do you do with them? You should certainly automate, or at least make as light touch as possible as many of your processes as possible. Automation of blocking / recategorisation of IP's/Domains which intelligence shows as being highly likely to be malicious could be blocked automatically with the understanding that its not always 100% accurate and may have an impact. 

Which other examples can you think of which would allow the automation of intelligence lead analysis to releave you, your team members and your organisation from what will become the Cyber Intelligence Tsunami?

 

Keywords: intelligence
3 comment(s)

Comments

Maybe some business-specific metric can put a 'cost' on the spam/attacks/scans you see, and weigh up against any known benefit (like sales/leads/signups) by accepting traffic from that prefix, network, country or whole region. This will always be different depending who your users/customers/audience might be, or even by traffic type (web/email/other).

You can try to correlate your own data with community resources to extend coverage and fill in the gaps.

With a simple cost-benefit cutoff you can decide what traffic to drop. You could also make it more of a priority to report abuse coming from the 'good' networks, whom you can't afford block, and may be more likely to actually follow up on it, too.
This was VERY informative Stephen. I think i'll create this at home first and get well versed in it so I can convince my boss I need a production Ubuntu LTS server to implement
collectiveintel.org/examples

/shameless-plug

:)

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