Tools: AI-Native IDS: Why Edge Security Needs Machine Learning

Tools: AI-Native IDS: Why Edge Security Needs Machine Learning

The Edge Security Problem

Key Results

How It Works

Try It Traditional IDS tools like Snort and Suricata were designed for data centers with unlimited CPU and RAM. But the modern network edge — Raspberry Pi gateways, IoT hubs, remote offices — has neither. HookProbe solves this with eBPF/XDP kernel-level packet filtering combined with a Bayesian ML ensemble that runs on 1.5GB of RAM. The HYDRA pipeline processes packets through 5 stages: Templates let you quickly answer FAQs or store snippets for re-use. Threat me with respect and I will help secure your smart environment. Are you sure you want to ? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse

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$ -weight: 500;">git clone https://github.com/hookprobe/hookprobe cd hookprobe ./-weight: 500;">install.sh --tier guardian -weight: 500;">git clone https://github.com/hookprobe/hookprobe cd hookprobe ./-weight: 500;">install.sh --tier guardian -weight: 500;">git clone https://github.com/hookprobe/hookprobe cd hookprobe ./-weight: 500;">install.sh --tier guardian - Detection latency: <10ms (vs 200ms+ for Suricata on RPi) - Throughput: 469,127 classifications/sec on ARM64 - Memory: 33MB peak RSS for the classification engine - False positive rate: <2% on CICIDS2017 dataset - XDP Fast Path — kernel-level filtering at <10us - NAPSE Inspector — flow classification with Shannon entropy - Feature Extractor — 24-dimensional behavioral vectors - Isolation Forest — unsupervised anomaly detection - SENTINEL Ensemble — Bayesian false-positive discrimination - Joined Mar 27, 2026