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iot-23-preprocessed-minimumcolumns

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train · 2.37k rows

id.resp_p int64 0 65.5k	proto stringclasses 3 values	conn_state stringclasses 13 values	orig_pkts int64 0 66M	orig_ip_bytes int64 0 1.91B	resp_ip_bytes int64 0 350M	label stringclasses 2 values
8,081	tcp	S0	2	80	0	Malicious
37,215	tcp	S0	2	80	0	Malicious
52,869	tcp	S0	2	80	0	Malicious
8,080	tcp	S0	2	80	0	Malicious
80	tcp	S0	2	80	0	Malicious
666	tcp	S0	6	360	0	Malicious
8,081	tcp	REJ	2	80	80	Benign
37,215	tcp	REJ	2	80	80	Malicious
666	tcp	S0	2	120	0	Malicious
52,869	tcp	REJ	2	80	80	Malicious
80	tcp	RSTO	4	160	88	Benign
80	tcp	S0	6	360	0	Malicious
80	tcp	REJ	2	80	80	Benign
80	tcp	RSTO	6	240	176	Benign
666	tcp	REJ	2	120	80	Malicious
123	udp	SF	2	152	152	Benign
0	icmp	OTH	2	136	0	Benign
1	icmp	OTH	2	112	0	Benign
1	icmp	OTH	2	144	0	Benign
10	icmp	OTH	2	136	0	Benign
666	tcp	REJ	6	360	80	Malicious
13	icmp	OTH	2	112	0	Benign
1	icmp	OTH	2	136	0	Benign
0	icmp	OTH	2	112	0	Benign
8,080	tcp	REJ	2	80	80	Benign
0	icmp	OTH	2	192	0	Benign
80	tcp	S0	4	160	0	Malicious
22	tcp	OTH	136	9,088	9,152	Benign
8,081	tcp	RSTO	4	160	88	Benign
8,081	tcp	S0	6	360	0	Malicious
13	icmp	OTH	2	136	0	Benign
52,869	tcp	S0	4	160	0	Malicious
22	tcp	SF	20	1,376	1,512	Benign
666	tcp	REJ	4	240	80	Malicious
37,215	tcp	S0	4	160	0	Malicious
3	icmp	OTH	2	136	0	Benign
8,081	tcp	RSTO	4	160	80	Benign
13	icmp	OTH	2	140	0	Benign
8,081	tcp	RSTO	8	320	264	Benign
17,576	tcp	REJ	2	88	80	Benign
17,576	tcp	RSTOS0	2	80	0	Benign
53	udp	S0	2	134	0	Benign
53	udp	SF	2	146	146	Benign
53	udp	SF	2	134	131	Benign
53	udp	SF	1	67	131	Benign
53	udp	S0	1	67	0	Benign
53	udp	SF	1	73	73	Benign
123	udp	S0	1	76	0	Benign
53	udp	SF	1	67	67	Benign
443	tcp	S0	3	180	0	Malicious
53	udp	SF	1	67	83	Benign
443	tcp	S0	1	60	0	Malicious
123	udp	SF	2	152	76	Benign
53	udp	S0	1	73	0	Benign
53	udp	SF	2	132	82	Benign
53	udp	SF	1	66	82	Benign
123	udp	SF	1	76	76	Benign
22	tcp	OTH	0	0	88	Benign
123	udp	S0	2	152	0	Benign
123	udp	SF	10	760	760	Benign
123	udp	SF	8	608	608	Benign
123	udp	SF	7	532	532	Benign
5,355	udp	SF	1	73	146	Benign
123	udp	SF	6	456	456	Benign
53	udp	SF	1	67	122	Benign
443	tcp	SF	8,295	348,808	366,587	Malicious
53	udp	S0	2	132	0	Benign
443	tcp	RSTR	3	1,600	224	Malicious
53	udp	S0	1	66	0	Benign
53	udp	S0	1	71	0	Benign
53	udp	SF	1	71	87	Benign
53	udp	S0	2	142	0	Benign
53	udp	S0	2	146	0	Benign
53	udp	SF	1	73	148	Benign
53	udp	SF	1	67	179	Benign
443	tcp	S1	8,872	372,893	388,975	Malicious
53	udp	SF	3	201	198	Benign
53	udp	SF	2	134	83	Benign
22	tcp	OTH	3	192	752	Benign
443	tcp	SF	6,793	285,885	301,794	Malicious
443	tcp	REJ	2	120	40	Malicious
53	udp	SF	2	132	132	Benign
53	udp	SF	2	142	142	Benign
53	udp	SF	2	134	134	Benign
0	icmp	OTH	68	5,672	0	Benign
443	tcp	S2	5,895	248,182	257,816	Malicious
443	tcp	S1	4,485	189,231	200,386	Malicious
8,081	tcp	S0	1	40	0	Malicious
37,215	tcp	S0	1	40	0	Malicious
52,869	tcp	S0	1	40	0	Malicious
8,080	tcp	S0	1	40	0	Benign
80	tcp	S0	1	40	0	Malicious
666	tcp	S0	3	180	0	Malicious
666	tcp	S0	1	60	0	Malicious
37,215	tcp	REJ	1	40	40	Malicious
8,080	tcp	REJ	1	40	40	Benign
8,080	tcp	RSTO	2	80	44	Benign
8,080	tcp	S0	3	180	0	Benign
1	icmp	OTH	1	56	0	Benign
0	icmp	OTH	1	56	0	Benign

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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.

This dataset only contains the 6 most important columns: responder's port, transport layer protocol, connection state, number of packets sent by the originator, number of IP level bytes that the originator sent, number of IP level bytes that the responder sent, and the malicious or bengin label. These columns were determined from research done by Alani & Miri in "Towards an Explainable Universal Feature Set for IoT Intrusion Detection." They determined that these columns alone give 98% accuracy. This means a light weight model can contain these column alone and still have non-trivial results. This dataset only contains 2.5k rows, as the duplicates have been dropped.

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.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: 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: 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_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 malicious.
Data type: string - enum(Malicious, Benign)

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”

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Number of rows:

2,370

Models trained or fine-tuned on 19kmunz/iot-23-preprocessed-minimumcolumns

GeorgeNhj/BERT_based_My_SecurityLLM

Text Classification • Updated Jan 10 • 7 • 1