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 Intrusion Detection Systems (IDS) for IoT Devices (Comparative Analysis of IoT Botnet Datasets).

The selection of the subset was determined by Aqeel Ahmed on Kaggle 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 processing performed by the dataset creators. See notes here for caviats for each column.

Expand for feature names, descriptions, and datatypes

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.

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”