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---
dataset_info:
features:
- name: ts
dtype: float64
- name: uid
dtype: string
- name: id.orig_h
dtype: string
- name: id.orig_p
dtype: int64
- name: id.resp_h
dtype: string
- name: id.resp_p
dtype: int64
- name: proto
dtype: string
- name: service
dtype: string
- name: duration
dtype: float64
- name: orig_bytes
dtype: int64
- name: resp_bytes
dtype: int64
- name: conn_state
dtype: string
- name: local_orig
dtype: float64
- name: local_resp
dtype: float64
- name: missed_bytes
dtype: int64
- name: history
dtype: string
- name: orig_pkts
dtype: int64
- name: orig_ip_bytes
dtype: int64
- name: resp_pkts
dtype: int64
- name: resp_ip_bytes
dtype: int64
- name: label
dtype: string
splits:
- name: train
num_bytes: 1232978140
num_examples: 6046623
download_size: 274218995
dataset_size: 1232978140
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- tabular-classification
- table-question-answering
language:
- en
tags:
- code
---
# Aposemat IoT-23 - a Labeled Dataset with Malcious and Benign Iot Network Traffic
**Homepage:** [https://www.stratosphereips.org/datasets-iot23](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 Intrusion Detection Systems (IDS) for IoT Devices [(Comparative Analysis of IoT Botnet Datasets)](https://doi.org/10.53070/bbd.1173687).
The selection of the subset was determined by [Aqeel Ahmed on Kaggle](https://www.kaggle.com/datasets/engraqeel/iot23preprocesseddata) and contains 6 million samples. The Kaggle upload, nor this one, have employed data balancing. The Kaggle card does not contain methodology to understand what criteria was used to select these samples. If you want ensure best practice, use this dataset to mock-up processing the data into a model before using the full dataset with data balancing. This will require processing the 8GB of conn.log.labelled files.
# Feature information:
All features originate from the [Zeek](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info) processing performed by the dataset creators. [See notes here for caviats for each column](https://docs.zeek.org/en/master/scripts/base/protocols/conn/main.zeek.html#type-Conn::Info).
<details>
<summary>Expand for feature names, descriptions, and datatypes</summary>
Name: ts
Desription: This is the time of the first packet.
Data Type: float64 - Timestamp
Name: uid
Description: A Zeek-defined unique identifier of the connection.
Data type: string
Name: id.orig_h
Description: The originator’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.orig_p
Description: The originator’s port number.
Data type: int64 - uint64 in original
Name: id.resp_h
Description: The responder’s IP address.
Data type: string - for the form 255.255.255.255 for IPv4 or [aaaa:bbbb:cccc:dddd:eeee:ffff:1111:2222] for IPv6
Name: id.resp_p
Description: The responder’s port number.
Data type: int64 - uint64 in original
Name: proto
Description: The transport layer protocol of the connection.
Data type: string - enum(unknown_transport, tcp, udp, icmp). Only TCP and UDP in subset
Name: service
Description: An identification of an application protocol being sent over the connection.
Data type: optional string
Name: duration
Description: How long the connection lasted.
Data type: optional float64 - time interval
Name: orig_bytes
Description: The number of payload bytes the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_bytes
Description:The number of payload bytes the responder sent.
Data type: optional int64 - uint64 in original
Name: conn_state
Description: Value indicating connection state. (S0, S1, SF, REJ, S2, S3, RSTO, RSTR, RSTOS0, RSTRH, SH, SHR, OTH)
Data type: optional string
Name: local_orig
Description: If the connection is originated locally, this value will be T. If it was originated remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: local_resp
Description: If the connection is responded to locally, this value will be T. If it was responded to remotely it will be F.
Data type: optional float64 - bool in original but null for all columns
Name: missed_bytes
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.
Data type: optional int64 - uint64 in original. default = 0
Name: history
Description: Records the state history of connections as a string of letters.
Data type: optional string
Name: orig_pkts
Description: Number of packets that the originator sent.
Data type: optional int64 - uint64 in original
Name: orig_ip_bytes
Description: Number of IP level bytes that the originator sent.
Data type: optional int64 - uint64 in original
Name: resp_pkts
Description: Number of packets that the responder sent.
Data type: optional int64 - uint64 in original
Name: resp_ip_bytes
Description: Number of IP level bytes that the responder sent.
Data type: optional int64 - uint64 in original
Name: label
Description: Specifies if data point is benign or some form of malicious. See the dataset creators paper for descriptions of attack types
Data type: string - enum('PartOfAHorizontalPortScan', 'Okiru', 'DDoS', 'C&C-HeartBeat',
'Benign', 'C&C-Torii', 'C&C', 'C&C-FileDownload', 'Okiru-Attack',
'Attack', 'FileDownload', 'C&C-HeartBeat-FileDownload',
'C&C-Mirai')
NOTE: ts, uid, id.orig_h, id.resp_h SHOULD BE removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h), as that can lead to over fitting to dataset specific times and addresses.
Further local_orig, local_resp SHOULD BE removed as they are null in all rows, so they are useless for training.
</details>
## Citation
If you are using this dataset for your research, please reference it as “Sebastian Garcia, Agustin Parmisano, & Maria Jose Erquiaga. (2020). IoT-23: A labeled dataset with malicious and benign IoT network traffic (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4743746”
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |