Hey everyone! Just made the switch to a security-focused DevOps role and I'm learning Firepower. Wanted to share how I'm testing new IPS policies before they hit production. Feedback welcome! 😅
I built a small lab with a couple of old servers. One runs a "traffic repeater" using tcpreplay. I capture benign production traffic (stripped of any PII) and then replay it through the FTD test box with the new policy. I use Grafana and a Prometheus exporter to visualize the impact.
Here's a snippet of the script that runs the test and pulls the drop counts from the FTD API:
```bash
# Get blocked packet count after test run
curl -s -k -X GET "https://ftd-test/api/.../snort/statistics"
-H "Authorization: Bearer $TOKEN" | jq '.items[].blockedPackets'
```
This lets me see if the new rules are blocking too much good traffic. The dashboard shows me blocked packets over time, CPU/memory on the FTD, and throughput. Helps me catch overly aggressive rules before they cause trouble in prod.
What do you think? How do you guys test your policy changes?
The tcpreplay approach is solid for behavioral verification, but you might be missing the latency and retransmission impact of those dropped packets. The blocked count is a good start, but actual user experience degradation often comes from the delays introduced by inspection, not just outright drops. Have you considered replaying the same capture with the policy disabled as a baseline and comparing the TCP session establishment times? That delta can tell you if a rule set, even with zero blocks, is adding problematic latency for critical applications.
Good catch on the baseline comparison. Latency can be the real killer.
I've run into this with API traffic. A rule that passes all packets but adds 50ms per inspection can blow through a service's SLA without a single drop showing up. My quick hack is to embed a timestamp in the first packet of a test flow and measure the round trip in the replayed response.
What do you use to measure the TCP session times? I've just been using Wireshark manually, which isn't great for scaling.
Capturing production traffic is a good start. But you need more than blocked counts.
Your method won't catch false negatives. A bad rule can let malicious traffic through because your benign capture doesn't contain attack patterns. You need a malicious payload library in your replay mix.
Also, what's your definition of "too much" good traffic? Is it a percentage? Does it correlate to specific applications? Just saying you catch overly aggressive rules is vague. Set a quantitative threshold in your dashboard, like "block rate > 0.1% on financial app traffic triggers a review."
Five nines? Prove it.