The Worm Blog recently pointed to an interesting paper, in which the detection and incidence handling of an SQL/Slammer outbreak in an enterprise is discussed. Remarkably, the enterprise actually had a reasonably secure setup. They knew about the new worm, thought they had a handle on it, and still were compromised.
This real-life incidence report represents a great example of why prior assumptions can lead to massive security failure. Note that all signature based systems rely on prior assumptions (the signatures). Also note that many security architectures are built on prior assumptions. When you read the paper, you can see that the assumptions made by the security staff are actually quite reasonable. Yet, these assumptions eventually led to failure, which just goes to show that additional intelligence in the network is needed.
The paper contains a lot of background information about the Slammer worm, as well as the network setup of that enterprise. If you don't want to read the whole thing, I would like to point you to the chapter 'Identification' which starts on page 19 and ends on page 21. If you just read those three pages, you can take away some very important points:
- Perimeter-based security is insufficient. A worm outbreak can happen from any point, even from within the network, as in this case.
- Assumptions about the direction of the attack (from the outside to the inside) meant that IDSs were deployed only for one-way monitoring, missing the scans from within the network. Assumptions are bad.
- Most importantly: Intelligence is needed when analyzing the network traffic. After long, fruitless debugging and trouble shooting sessions, one of their engineers finally noted that the network traffic profile looked different. An intelligent anomaly detection solution would have had this information instantaneously.
All in all, it took the business some four hours to conclude with certainty that they had been compromised and were fighting a worm outbreak. Four hours, in which servers were slow to respond, had to be rebooted frequently, and during which their tech-support had to field numerous calls by customers, complaining that the services were slow.
Contrast this with the situation as it would have presented itself with an intelligent anomaly detection system in place:
- An infected machine emerges somewhere within the network.
- The intelligent anomaly detection solution recognizes this immediately (note that Esphion's netDeFlect can even detect slow-scanning worms in seconds).
- Alerts are sent to the administrators, packet samples are presented and ACLs are suggested, automatically, and within seconds of the onset of the attack.
Only seconds have elapsed from outbreak to detection, analysis and start of containment. Is this fast enough? It is, as I had discussed here.
So, in conclusion, the paper presents an excellent case why any organization, which really wants to protect their uptime and assets, should consider the deployment of an intelligent anomaly detection solution, right in the middle of their network.