pcap2tensor

PCAP → ML tensor extraction for network intrusion detection research.

A fast, streaming, production-grade Python library for turning raw packet captures into training-ready tensors. Built for NIDS researchers tired of rolling their own extraction pipeline for every paper.

Why this exists

Every ML-based network intrusion detection paper reinvents the same pipeline:

1. Parse a PCAP
2. Extract per-packet features (size, inter-arrival time, direction, TCP flags, ...)
3. Slide a window across the sequence
4. Save as a tensor

Every implementation is a one-file script that doesn't handle PCAPs larger than RAM, doesn't expose clean extension points for custom features, and quietly crashes on the first malformed packet. pcap2tensor is that pipeline, packaged properly — streaming, extensible, and published on PyPI.

Install

pip install pcap2tensor

Python ≥ 3.9. Depends on Scapy, PyTorch, NumPy, tqdm.

Quickstart

frompcap2tensorimportextracttensor=extract("capture.pcap", features="aegis-6d", window_size=1000, stride=500)
print(tensor.shape) # torch.Size([num_windows, 1000, 6])

Feed straight into any sequence model — Transformer, LSTM, SSM, CNN.

Large PCAPs

Streaming chunked processing — never loads the full PCAP into memory:

frompcap2tensorimportPCAPExtractorextractor=PCAPExtractor(
features="aegis-6d",
window_size=1000,
stride=500,
chunk_size=2_000_000, # flush every 2M packets
)
# Option A: save chunked .pt filesextractor.save("massive.pcap", output_dir="./tensors/")
# Option B: stream chunks into your training loopforchunkinextractor.extract_chunks("massive.pcap"):
train_step(chunk)

Parallel batch

frompcap2tensorimportbatch_extractbatch_extract("./pcaps/", output_dir="./tensors/", features="aegis-6d", workers=8)

From the CLI:

pcap2tensor batch ./pcaps/ -o ./tensors/ -n 8

Feature presets

Preset	Dim	Features
basic-3d	3	size, IAT, direction
aegis-6d	6	size, IAT, direction, TCP window, TCP flags, payload ratio
extended-10d	10	aegis-6d + protocol one-hot (TCP/UDP/ICMP/other)
full-13d	13	extended-10d + destination port category (well-known/registered/dynamic)

The aegis-6d preset matches the feature set in AEGIS (Ferrel, 2026) — a TVD-HL-SSM architecture achieving F1 0.9952 on encrypted traffic detection at 262 μs inference latency.

Custom features

A Feature is any stateful callable returning a float or a flat list of floats. Subclass Feature, implement __call__, optionally override reset if you hold state:

importmathfromcollectionsimportCounterfromscapy.layers.inetimportTCPfrompcap2tensorimportPCAPExtractor, Feature, Size, IAT, DirectionclassPayloadEntropy(Feature):
name="payload_entropy"dim=1def__call__(self, pkt):
payload=bytes(pkt[TCP].payload) ifTCPinpktelseb""ifnotpayload:
return0.0counts=Counter(payload)
n=len(payload)
return-sum((c/n) *math.log2(c/n) forcincounts.values()) /8.0extractor=PCAPExtractor(
features=[Size(), IAT(), Direction(), PayloadEntropy()],
)
tensor=extractor.extract("capture.pcap")

Return a list[float] and set dim accordingly for multi-valued features (e.g. one-hots).

CLI

# Single PCAP
pcap2tensor extract capture.pcap -o ./tensors/
# Parallel batch over a directory
pcap2tensor batch ./pcaps/ -o ./tensors/ -n 8
# List presets
pcap2tensor presets
# Override everything
pcap2tensor extract capture.pcap -f extended-10d -w 2000 -s 1000 -c 5000000

Design

Concern	How it's handled
Memory	Streaming PcapReader, chunked flush every chunk_size packets
Malformed packets	Caught per-packet, silently skipped — a 4-hour run doesn't die on one pkt
Flow state	Per-Feature instance, auto-reset between PCAPs
Parallelism	ProcessPoolExecutor for batch mode
IPv6	First-class (IPv6 src/dst, port extraction, protocol number)
Reproducibility	Same PCAP + same config = bit-identical tensor output
Output format	PyTorch .pt on disk, torch.Tensor in memory

Performance

Rough single-core throughput with aegis-6d on a modern x86 machine: roughly 50–120k packets/sec, TCP-heavy captures slower than UDP-heavy. With 8 workers in batch mode, processing 100 GB+ of PCAPs per hour is achievable.

Your bottleneck is Scapy parsing, not feature extraction.

Output shape

Every extractor produces tensors of shape:

(num_windows, window_size, feature_dim)

where feature_dim = sum(f.dim for f in features). For aegis-6d, that's 6.

Citation

If you use this library in research, please cite the companion paper:

@article{ferrel2026aegis,
title = {AEGIS: Adversarial Entropy-Guided Immune System -- Thermodynamic State Space Models for Zero-Day Network Evasion Detection},
author = {Ferrel, Vickson},
journal = {arXiv preprint arXiv:2604.02149},
year = {2026},
url = {https://arxiv.org/abs/2604.02149}
}

License

MIT © Vickson Ferrel — Vixero Technology Enterprise

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