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Experimental eBPF Firewall in Rust with Heuristic Risk Scoring

Hacker News

Experimental eBPF Firewall in Rust with Heuristic Risk Scoring

I’ve been working on a small experimental eBPF/XDP-based firewall written in Rust (using Aya). It’s not a full IPS and not an antivirus — it’s a packet-level reputation engine for learning purposes. What it currently does Rust + eBPF (XDP) packet inspection Per-IP risk scoring (0–1000) Detects: Port scans: SYN/FIN/NULL/XMAS TTL variance (possible spoofing) Burst traffic patterns (DoS-like) ICMP misuse and oversized payloads Malformed packets Bloom filter blocklist (up to 400k entries) LRU map tracking port access timing Auto-blocking on critical risk Tarpit behavior on high-risk IPs Why I built it I wanted to learn: Rust in kernel-level constraints How XDP works internally Practical heuristics for lightweight threat detection Efficient state tracking inside eBPF maps What it is NOT Not production-ready Not a malware scanner Not a Suricata/Snort alternative Looking for feedback Particularly interested in: Map design improvements Heuristic tuning Performance considerations Ideas to avoid false positives Repo: https://github.com/N1ghttm4r33/Antivirus/tree/main

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: ttm · Missing: arr, mrr, revenue
15%15% 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.

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