Passive network anomaly detection
A spiking neural network for the wire
Alerts you can replay.
Byte for byte.
Rift is a passive anomaly detector. It observes network telemetry, classifies it against a trained baseline, and emits per-class confidence scores — returning every packet untouched. A Q16.16 fixed-point spiking neural network with no floats in its tick path: the same brain produces the same scores on x86_64, ARM, and eBPF.
A detector.
Not a firewall.
Rift watches network telemetry and classifies it against a trained baseline. Its output is per-class
confidence scores — nothing more. The eBPF program returns XDP_PASS on every packet it sees,
and there is no code path from Rift's output back to the wire.
Sixteen features are extracted per tick — volume, rate, protocol mix, SYN/FIN/RST counts, Bloom-cardinality of source and destination IPs, packet-size statistics, ACK ratio — then a bipolar surprise encoder turns each feature into input bits, and the network ticks:
Telemetry → 16 features → Bipolar surprise encoder → SNN tick (leak · afferent · recurrent · threshold · spike) → Class score accumulators → Per-class confidence scores
Same bytes on every platform.
Bit-exactness is a hard requirement, not a goal. Every arithmetic operation in the tick path is Q16.16 fixed point — the engine source is checked for floating point on every commit. Replay the same brain on the same input and you get the same scores, everywhere.
Nothing between you
and the wire.
Rift is not a mitigation engine. It does not drop, redirect, throttle, or modify packets. Its alerts are advisory input to a host firewall whose own rules remain authoritative.
Watch your traffic with a detector that keeps its word.
Rift is Apache-2.0 licensed, on GitHub. Clone it, build it, and replay the reference capture — the expected SHA-256 is in the repository.
The repository: github.com/notnullai/rift.
KHALM