README.md

---
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:
- text-classification
- question-answering
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 contained 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: string  

Name: duration  
Description: How long the connection lasted.  
Data type: float64 - time interval  

Name: orig_bytes  
Description: The number of payload bytes the originator sent.  
Data type: int64 - uint64 in original  

Name: resp_bytes  
Description:The number of payload bytes the responder sent.  
Data type: 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: string  

Name: missed_bytes  
Description: Indicates the number of bytes missed in content gaps, which is representative of packet loss.  
Data type: int64 - uint64 in original  

Name: history  
Description: Records the state history of connections as a string of letters.   
Data type: string  

Name: orig_pkts  
Description: Number of packets that the originator sent.  
Data type: int64 - uint64 in original  

Name: orig_ip_bytes  
Description: Number of IP level bytes that the originator sent.  
Data type: int64 - uint64 in original  

Name: resp_pkts  
Description: Number of packets that the responder sent.  
Data type: int64 - uint64 in original  

Name: resp_ip_bytes  
Description: Number of IP level bytes that the responder sent.  
Data type: int64 - uint64 in original  

Name: label  
Description: Specifies if data point is malicious or benign  
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”
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)