datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CIC-IoT-2023TON_IoT_network
TON IoT Network
The TON IoT train test network dataset provided by https://research.unsw.edu.au/projects/toniot-datasets
Dataset Details
The datasets have been called 'ToN_IoT' as they include heterogeneous data sources collected from Telemetry datasets of IoT and IIoT sensors, Operating systems datasets of Windows 7 and 10 as well as Ubuntu 14 and 18 TLS and Network traffic datasets. The datasets were collected from a realistic and large-scale network designed at the… See the full description on the dataset page: https://huggingface.co/datasets/codymlewis/TON_IoT_network.CIC-IoT-2023
CIC-IoT-2023 IoT Intrusion Detection Dataset
The CICIoT2023 dataset from the Canadian Institute for Cybersecurity, subsampled and preprocessed for machine learning evaluation.
Configurations
random_3way (default) — 80/10/10 Three-Way Split
Stratified random split with fully separated train/test/validation sets:
Train (80%): Model training and architecture search
Test (10%): Threshold calibration (held out from training)
Validation (10%): Final reported metrics… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023.EnergyBench
EnergyBench Dataset
Overview
A open-source large-scale energy meter dataset designed to support a variety of energy analytics applications, including load profile analysis, and load forecasting.
It compiles over 60 detailed electricity consumption datasets encompassing approximately 78,037 real buildings representing the global building stock, offering insights into the temporal and spatial variations in energy consumption.
The buildings are classified into two… See the full description on the dataset page: https://huggingface.co/datasets/ai-iot/EnergyBench.IOT23-PARQUET
IoT-23 — canonical flow parquet (light path)
IoT-23 (Stratosphere
Laboratory, CTU University: real IoT malware infections + benign IoT device
captures) converted from the light distribution's labeled Zeek conn logs
into a canonical flow-record parquet schema: 23 captures, 325.3M rows,
7.8 GB zstd. One parquet per capture — leave-one-capture-out splits rebuild
from filenames.
Fidelity caveats, by construction (Tier A only):
Source is conn.log.labeled, not pcap: TCP flag… See the full description on the dataset page: https://huggingface.co/datasets/Lystea/IOT23-PARQUET.NF-ToN-IoT-v2CIC-IoT-2023-neto-full
CIC-IoT-2023 — Neto-Full (Authoritative 46.7M)
This is the authoritative canonical CIC-IoT-2023 dataset at the row count
published by Neto et al. (2023): 46,686,579 rows × 46 features.
Sourced from the Kaggle mirror akashdogra/ciciot23csv (13.75 GB single CSV)
which itself was derived from CIC's official 169-file distribution. Compared
to bencorn's HF mirror (45M, 39 features), this preserves:
All 46 original features (bencorn dropped 7)
The full 46.7M row count (bencorn re-merge… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023-neto-full.CIC-IoT-2023
CIC-IoT-2023 IoT Intrusion Detection Dataset
The CICIoT2023 dataset from the Canadian Institute for Cybersecurity, subsampled and preprocessed for machine learning evaluation.
Configurations
random_3way (default) — 80/10/10 Three-Way Split
Stratified random split with fully separated train/test/validation sets:
Train (80%): Model training and architecture search
Test (10%): Threshold calibration (held out from training)
Validation (10%): Final reported metrics… See the full description on the dataset page: https://huggingface.co/datasets/siyam21/CIC-IoT-2023.CIC-IoT-2023-neto-subsample
CIC-IoT-2023 — Neto-Subsample (1.3M, 46-feature canonical)
Stratified subsample (~1,429,753 rows) of the canonical Neto 46.7M
dataset (lacg030175/CIC-IoT-2023-neto-full). Same 46-feature schema as the
full version. Drop-in replacement for lacg030175/CIC-IoT-2023 (1.3M
bencorn-derived, 39 features) for new experiments needing the canonical
feature set.
Subsample composition:
Benign: 200,000 rows
Each attack subclass: up to 50,000 rows
NaN/Inf preserved (no dropna). Pair with… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023-neto-subsample.iot-sensor-telemetry
IoT Sensor Telemetry (Synthetic) (Free Sample)
This is a free sample with 5,003 rows. The full dataset has 50,006 rows across 3 tables.
High-frequency telemetry from a simulated smart factory operating three
CNC lines, a finishing cell, and a predictive-maintenance program over
six months. Covers temperature, humidity, pressure, and vibration sensors
sampled on a rolling 5-minute schedule with realistic shift patterns,
machine assignments, maintenance alerts, and operational state… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/iot-sensor-telemetry.CIC-IoT-2023-canonical-neto
CIC-IoT-2023 — Canonical (Neto et al.) Variant
This is the canonical CIC-IoT-2023 dataset, sourced from
bencorn/CIC-IoT-2023's
CSV/MERGED_CSV/ folder, which contains Neto et al.'s authentic merged CSVs
WITH embedded labels (vs. bencorn's other CSV/CSV/<attack>/ re-organization
which lost ~6.5M rows during the folder-restructure).
Why this exists: prior lacg030175/CIC-IoT-2023-full and -full-raw were
built from CSV/CSV/ and contained only 38.5M rows. This one contains
~45,019,243… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023-canonical-neto.CIC-IoT-2023-full
CIC-IoT-2023 Full Dataset (46M+ rows)
The FULL CICIoT2023 dataset — all 38,508,041 rows, no subsampling.
Configuration
random_3way (default) — 80/10/10 Three-Way Split
Stratified random split with fully separated sets:
Train (80%): 30,806,432 rows — model training and architecture search
Test (10%): 3,850,804 rows — threshold calibration (held out from training)
Validation (10%): 3,850,805 rows — final reported metrics (never touched)
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023-full.omnimcp_smartenergy_iot_teaser
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📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_smartenergy_iot_teaser.iotids-bot-iotiot-23-preprocessed-allcolumns
Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
Homepage: https://www.stratosphereips.org/datasets-iot23
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for… See the full description on the dataset page: https://huggingface.co/datasets/19kmunz/iot-23-preprocessed-allcolumns.rt-iot2022iot-23-preprocessed
Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
Homepage: https://www.stratosphereips.org/datasets-iot23
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for… See the full description on the dataset page: https://huggingface.co/datasets/19kmunz/iot-23-preprocessed.NF-ToN-IoT-v2.csv
Dataset Card for NF-ToN-IoT Network Flow Dataset
Dataset Description
Dataset Summary
NF-ToN-IoT is a network flow dataset derived from IoT network traffic, containing both benign and attack flows. The dataset was created by converting pcap files from the ToN-IoT testbed into NetFlow records, providing labeled data for training network intrusion detection systems.
Size:
Total flows: 11,858,887
Benign samples: 4,270,402 (36.01%)
Scanning samples: 2,646,685… See the full description on the dataset page: https://huggingface.co/datasets/Hmehdi515/NF-ToN-IoT-v2.csv.CIC-IoT-2023
CIC-IoT-2023 IoT Intrusion Detection Dataset
The CICIoT2023 dataset from the Canadian Institute for Cybersecurity, subsampled and preprocessed for machine learning evaluation.
Configurations
random_3way (default) — 80/10/10 Three-Way Split
Stratified random split with fully separated train/test/validation sets:
Train (80%): Model training and architecture search
Test (10%): Threshold calibration (held out from training)
Validation (10%): Final reported metrics… See the full description on the dataset page: https://huggingface.co/datasets/casscloud/CIC-IoT-2023.iot-23-preprocessed-minimumcolumns
Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
Homepage: https://www.stratosphereips.org/datasets-iot23
This dataset contains a subset of the data from 20 captures of Malcious network traffic and 3 captures from live Benign Traffic on Internet of Things (IoT) devices. Created by Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga at the Avast AIC laboratory with the funding of Avast Software, this dataset is one of the best in the field for… See the full description on the dataset page: https://huggingface.co/datasets/19kmunz/iot-23-preprocessed-minimumcolumns.pi5-agricultural-iot-32day
Pi5 Agricultural IoT — 32-Day Continuous Performance Dataset
Dataset Summary
This dataset contains 9,409 system performance records collected from a Raspberry Pi 5 (8 GB) deployed as an edge computing node at an agricultural farm in Biên Hòa, Đồng Nai, Vietnam. The device managed 10 simultaneous IP camera streams, performing real-time RTSP→RTMP transcoding via FFmpeg, orchestrated by 10 independent systemd services.
Data was collected continuously for 32 days (23/03/2026… See the full description on the dataset page: https://huggingface.co/datasets/talab-ai/pi5-agricultural-iot-32day.CIC-IoT-2023-full
CIC-IoT-2023 Full Dataset (46M+ rows)
The FULL CICIoT2023 dataset — all 38,508,041 rows, no subsampling.
Configuration
random_3way (default) — 80/10/10 Three-Way Split
Stratified random split with fully separated sets:
Train (80%): 30,806,432 rows — model training and architecture search
Test (10%): 3,850,804 rows — threshold calibration (held out from training)
Validation (10%): 3,850,805 rows — final reported metrics (never touched)
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/binhuang0013/CIC-IoT-2023-full.MOENV_IoT_PM2.5IoT-based-SmartParkingSystem-datasetAbout 2 years of IoT-based smart parking lot usage data collected on ThingSpeak from a system of IR sensor and ESP32 board, which has the slot availability information with respect to timestamps.
created_at = entry timestamp, field1 = parking slot ID, field2 = availability
CIC-IoT-2023-raw
CIC-IoT-2023 (1.3M subsample, raw variant)
Companion to lacg030175/CIC-IoT-2023. This variant preserves rows with NaN or ±infinity values in any feature column (the original dataset drops them via pd.dropna). Intended for use with ThermometerEncoder(invalid_encoding="single_bit"), which treats missing / undefined values as a learnable is_invalid flag bit rather than silently encoding them as zero.
Row counts
Full dataset: 1,342,371 rows, 50 (0.004%) with NaN in numeric… See the full description on the dataset page: https://huggingface.co/datasets/lacg030175/CIC-IoT-2023-raw.bot-iotiot-securityIoT Data Collection
This document provides detailed information about the various data fields collected by the IoT Device with Multiple Sensors. Each field is described along with the type of data it includes.
Data Fields
ALS (Ambient Light Sensor)
Explanation: Measures the ambient light levels around the thermostat.
Data Type: Intensity of light in lux (lumens per square meter).
PIR (Passive Infrared Sensor)
Explanation: Detects motion by measuring the infrared (heat) emitted by objects in… See the full description on the dataset page: https://huggingface.co/datasets/fenar/iot-security.dml-fl-iot-ids
DML-FL IoT IDS Benchmark Results
Results from the paper: Dynamic Multilevel Clustered Federated Learning for IoT Intrusion Detection
Grid (72 total runs)
Dimension
Options
Models
CNN, BiLSTM, CNN+BiLSTM, Transformer (fixed)
FL Algorithms
FedAvg, FedProx, FedNova
Datasets
CICIDS2017, UNSW-NB15, Edge-IIoTset
Distributions
IID, Non-IID (Dirichlet α=0.5)
Clients / Rounds
30 clients / 30 rounds / 3 local epochs
Files
File… See the full description on the dataset page: https://huggingface.co/datasets/JabaleNurAdnan/dml-fl-iot-ids.random-iot-sensor-readings
Random IoT Sensor Readings
8,000 synthetic minute-level IoT sensor readings (temperature, humidity, pressure, status). Purely random, for testing.
Note: This is randomly generated synthetic data with no real-world meaning. Generated for testing and demonstration purposes.