Generate Blocklists by ASN

Hacker News

I made this to scratch my own itch. When I get DDoS'd I can relieve the pressure very easily by blocking Cloudflare, ChinaNet, DigitalOcean, Linode, Hetzner and OVH. The problem is, it's difficult to get the up-to-date subnets associated with those hosts in bulk. I stumbled upon a free source of AS data a while ago, and integrated it into my custom OpenResty/Lua firewall. But I wanted a quick way to block an ASN earlier in the process (iptables), mostly to keep my HTTP logs cleaner during a DDoS. And thus, ASN Tool was born. I am running it on a cheap DO box with OpenResty/Lua as the backend. It seems to handle about 500 requests/second, which I hope is good enough for the time being. After this https://krebsonsecurity.com/2022/12/six-charged-in-mass-take... I haven't been hit by any DDoS attacks, so this might be all for naught. But I'm putting it out there anyway. Under the "Info" section, you can download the data file to run ASN lookups yourself. I suspect bandwidth may be an issue in the future. If anyone wants to sponsor hosting the file, email me at rumpelstiltskin at asntool.

Visit product View on Hacker NewsFeb 2, 2023

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: email, open · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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