Datasets:

19kmunz
/

iot-23-preprocessed

	---
	dataset_info:
	features:
	- name: id.orig_p
	dtype: int64
	- 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: 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: 93994789
	num_examples: 819024
	download_size: 11805369
	dataset_size: 93994789
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	task_categories:
	- question-answering
	- tabular-classification
	language:
	- en
	tags:
	- code
	pretty_name: d
	---
	# 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.

	This dataset only notes if the data is Malcious or Benign. The original dataset labels the type of malcious traffic aswell. This means this processing of the dataset is only suited for binary classification.

	# 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: id.orig_p
	Description: The originator’s port number.
	Data type: int64 - uint64 in original

	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: 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(Malicious, Benign)

	NOTE: ts, uid, id.orig_h, id.resp_h have been removed as they are dataset specific. Models should not be trained with specific timestamps or IP addresses (id.orig_h) using this dataset, as that can lead to over fitting to dataset specific times and addresses.
	Further local_orig, local_resp have been 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”