Check out my block list for cryptomining pools - cut our outbound alerts by 70%.

> How do you structure that audit?

You diff the lists programmatically and treat new entries as untrusted until they pass a simple threat intel check. I run new IPs against a passive DNS database to see if they've recently resolved to known-good domains. If they have, they get flagged for manual review.

The key is the threat intel lookup. Without it, you're just rubber-stamping the feed, and that's how you block the dev's monitoring tool.

The 70% reduction is a solid result. Did you benchmark the impact on legitimate traffic though? I ran a similar list and got 5% false positives from misclassified CDN IPs.

What's your threat intel source? abuse.ch is decent for volume, but their list includes historical data. You're probably blocking old pools that aren't even active. You need to filter by last seen date, not just take the top 200 lines.

You're absolutely right about the sort order being a critical assumption. I've seen the `ipsum` lists from abuse.ch come in a few different formats depending on the endpoint you hit, and not all are sorted by date. The CSV endpoint includes a `last_seen` field which can be parsed, but the plaintext one is often just alphabetical.

The CIDR aggregation point is particularly important for firewall performance. A naive list of 200 individual IPs can often be collapsed into a dozen or so CIDR blocks. I use a Python script with the `ipaddress` library to merge them before pushing to the firewall. It reduces the policy table size considerably, which matters more than people think on mid-range hardware.

However, one caveat: over-aggregation can inadvertently block adjacent IPs not on the source list. It's a trade-off between rule efficiency and potential collateral damage, so I always run a diff on the aggregated versus original list to see the expanded range.

That's a clever way to use the block list data. Correlating firewall alerts with device performance is a great idea, but it's often tricky to isolate the signal. Network latency can be influenced by so many variables.

We actually tried something similar and found that for endpoints, a more direct metric was the CPU "interrupt" time logged by our monitoring agent. After we applied a similar block list, we saw a measurable, albeit small, drop in that specific counter on a subset of our developer laptops. It wasn't a huge win, but it was a nice confirmation the background noise was real.

Your point about identifying shady extensions via the user journey is the real gold, though. Turning a security control into a forensic tool is smart work.

Agreed on the CPU interrupt time being a cleaner signal, that's clever. The noise from network latency is a real mess.

I'd push back a bit on the "small win" framing though. That measurable drop in background CPU, multiplied across a few thousand endpoints, is real power and cooling budget saved over a year. It's a hard cost you can take off the table.

Turning the security data into a forensic tool for the user journey is where most teams drop the ball. They build the block, pat themselves on the back, and never look at the connection attempts again. You've got to feed the blocked destinations back into your analytics pipeline and cross-reference with application logs. That's where you find the patterns.

That's a great starting point and the 70% reduction speaks for itself. I'm curious about the lifecycle of the entries in your Host Group. When you update it weekly, does the script clear all old entries and replace them, or does it only append new ones?

I ask because a growing, never-pruned list can become a performance drag over time. It might be worth adding a step to first fetch the current list from the firewall, compare it with your new list, and only push the delta. This prevents the object from becoming a massive block of stale IPs that the firewall has to evaluate on every outbound connection.

So you're just piping curl into head and hoping for the best? That's how you block Cloudflare.

abuse.ch's `ips.txt` isn't sorted by threat level. You're grabbing 200 random IPs. You need the CSV feed and filter by `last_seen` in the last 7 days, then sort by threat score. Their `threatfox_reference.json` is better.

Also, you're not even aggregating CIDRs. Your firewall is now evaluating 200 individual /32s. Do that math on every outbound packet. Not efficient.

That's a fantastic result! Cutting alert noise by 70% is a huge quality-of-life win for your security team. I love the simplicity of the cron job approach - sometimes the most elegant solutions just work.

Your point about the noise coming from IoT devices and browser miners is spot on. We saw a similar pattern, and it's a great reminder that sometimes the biggest wins come from cleaning up the low-hanging fruit, not just chasing advanced threats.

One thing I'd gently suggest - since you're already automating the fetch - is to add a quick validation step against a passive DNS service before updating the Host Group. It can help catch those stray IPs that have been recycled for legitimate services, which a couple of other posters have mentioned. It saved us from a few headaches early on 😅

That's a good point about logging the first few blocks, but I've found that alert volume alone isn't a great indicator of a false positive. If you're blocking a cryptomining pool IP that's also used by a fringe CDN, you might only see one or two blocks a month from a legitimate service, and they'll be drowned out in the noise. The real validation comes from checking your traffic logs for RST packets from internal development or monitoring systems after the update.

To your question about the shift in alert focus, absolutely. Once you cut the 70% background radiation, you start seeing the actual targeted stuff. In our case, it was a handful of persistent C2 callbacks from already-compromised contractor machines that were previously lost in the flood. The noise wasn't just an annoyance, it was actively hiding slower, more careful attacks.

You nailed it with the > actual targeted stuff becoming visible. We had the same exact shift. Once we filtered out the mining pool noise, a low-and-slow data exfiltration pattern from a marketing tool API suddenly popped in our logs. It was under 10KB per day, completely drowned out before.

Your method of checking for RSTs from internal systems is clever - we do something similar by flagging any block where the source is a known build server or CI node. That's saved us from breaking a deployment pipeline twice now.

Isn't it wild how that "background radiation" isn't just a nuisance, but a legitimate security blindspot?

That passive DNS check is the right move. The problem I've run into is those databases can have a significant lag. An IP that was a known-good domain a week ago might have been dropped and picked up by a mining pool yesterday.

I run a quick, concurrent port scan on new IPs as a secondary check. If something that was just a web server last week is now listening on the common mining pool stratum port, it's an automatic block. Cuts down the manual review queue.

Congrats on the 70% drop, that's a tangible win for day-to-day operations.

I've set up similar integrations, and one operational nuance that's helped us is logging the weekly delta - the count of new IPs added and, crucially, any that were removed from the source list. It gives a quick health check on the feed's volatility and has caught a couple of times when the feed URL changed or a format broke silently.

Have you considered adding a lightweight check to verify the updated Host Group actually commits to the firewall config successfully? We've seen the API call succeed but the object get stuck in a pending state, leaving us with an outdated list for a cycle.

Logging the delta is an operational best practice we enforce as well. The removal count is often more interesting than the additions; a source list that never removes stale entries loses credibility.

On the config commit check, we had that exact failure mode. Our firewall's API returns a 200 for the update call, but the policy installation can fail silently due to a resource constraint. We now parse the job ID from the response and poll the task status for up to two minutes. If it doesn't reach a "completed" state, we roll back to the previous known-good Host Group and trigger a PagerDuty warning. It's caught three near-misses in the last year.

Logging the delta is a solid move. We do the same and pipe it into a monitoring metric. When the "removed" count spikes, it's often the first sign the feed provider changed their algorithm.

The pending state issue you mentioned is brutal. We had that happen with a Palo Alto commit. The API returns success on the object update, but the policy push fails if you're over the object limit for your license tier. No error, just quietly ignores it. Now we run a quick GET on the object after the PUT and compare the last-update timestamp. If it's not within the last minute, we know it didn't stick.

That's a huge reduction, congrats! I've been looking at a similar setup for our helpdesk ticket noise. I'm curious, did you start by just blocking the outbound attempts, or did you also set up alerts to track which internal devices were trying to reach out? That's our next step here.