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asana17/ai_can_anomaly_detection_data

ai_can_anomaly_detection_data The rows the detectors in asana17/ai_can_anomaly_detection are trained, calibrated and tested on, built from CAN logs by assemble.dataset there. A run reads them at one revision and records that revision, so the models in asana17/ai_can_anomaly_detection_runs each name the data they were fitted on. python3 -m evaluate.pc.run asana17/ai_can_anomaly_detection_data <revision> out runs_clone main holds the dataset built from every log. A smaller one… See the full description on the dataset page: https://huggingface.co/datasets/asana17/ai_can_anomaly_detection_data.

sourceHugging Facecc-by-4.0updated 11d agoView on Hugging Face

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aicananomalydetectiondata

The rows the detectors in asana17/ai_can_anomaly_detection are trained, calibrated and tested on, built from CAN logs by assemble.dataset there. A run reads them at one revision and records that revision, so the models in asana17/ai_can_anomaly_detection_runs each name the data they were fitted on.

python3 -m evaluate.pc.run asana17/ai_can_anomaly_detection_data <revision> out runs_clone

main holds the dataset built from every log. A smaller one built for a quick try goes to a branch of its own.

Source

Built from the University of Turku J1939 truck dataset, a Renault Euro VI truck on the road, normal traffic only. It is CC BY 4.0, and so is this. https://etsin.fairdata.fi/dataset/7586f24f-c91b-41df-92af-283524de8b3e/data

The raw logs are not here. Fetch them from the page above.

Files

file	holds
`seconds.json`	`MIN_SPEED`, and each log's seconds above it, which splits the logs into train and test
`grid.json`	the train logs and the grid settings the grid was built with
`grid_raw.npy`	the train logs on a 0.1 s grid, float32, 17 signals a row. The train and calibration rows are cut from it
`grid_t.npy`, `grid_seg.npy`	each grid row's time and segment
`scale.npy`	the mean, then the std, every row is z-scored by, fitted to the train rows
`built.json`	the logs and the settings the attack set and the scale were built with
`attacked.json`	where each attack sits, its PGN, time span, rows and how far it moved them
`attacked_raw.npy`	the test logs with one attack each, on the grid
`attacked_rows.npy`	`attacked_raw.npy` z-scored by `scale.npy`
`attacked_t.npy`, `attacked_seg.npy`, `attacked_label.npy`, `attacked_wheel.npy`	each test row's time, segment, attack label and wheel speed

The signals and the settings are in grid.json and built.json.

Frames

frames/ holds the same attacked test logs frame by frame, for sending the attacks over a real CAN bus. They go with the rows above, the attack set the runs 20260916-001002, 20260916-064753, 20260916-234726 and 20260919-025203 scored.

file	holds
`frames/frames.parquet`	every test log's frames with its attack in, one row group per log
`frames/attacked.json`	`attacked.json` with each attack's log added

column	holds
`log`	the source log, such as `part_3/20210204093406279004.csv`
`timestamp`	epoch seconds
`can_id`	the 29-bit identifier
`data`	the payload bytes
`attacked`	True where the attack changed the payload

A frame can change while no row moves, so score a detector on attacked_label.npy, not on attacked. A row cut short in the source log was dropped on reading.