--- 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).
Expand for feature names, descriptions, and datatypes 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.
## 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)