IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023)

data category: Cybersecurity and anomaly detection benchmarks · 0 papers in the last 7 days

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Network traffic, intrusion, fraud and anomaly records used to compare detection methods.

Names searched for: Bot-IoT, TON_IoT, ToN-IoT, CICIoT2023, Edge-IIoTset

Key findings#

From Nov 2023 to Oct 2026, 87 archived papers mentioned IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Apr 2025 (9).
No paper of the tracked journals mentioned IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) in the last 30 days (2026-10-07 to 2026-11-05).
From 2017 to 2026 (so far), IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) was mentioned in 102 papers in the tracked journals, most of them in AI (81), information systems (19) and operations research (1).
46 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were UNSW Sydney (4), Beijing University of Posts and Telecommunications (3) and The University of Sydney (3).
The journals publishing most of them were Artificial Intelligence Review (15), Expert Systems with Applications (15) and Computers & Security (10).
The most-cited of them is “Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction” (Journal Of Big Data, 2024), with 315 citations.
Papers use IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) most often with CIC-IDS2017 / CSE-CIC-IDS2018 (43), NSL-KDD / KDD Cup 99 (36) and UNSW-NB15 (36); it has 8 partners in the code and data network, in the group around NSL-KDD / KDD Cup 99, UNSW-NB15, CIC-IDS2017 /….

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	18	41	28	87

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	0	1	3	6	15	38	18
Information Systems	2	1	1	1	6	7	1
Operations Research	0	0	0	0	0	1	0
Finance	0	0	0	0	0	0	1
All categories	2	2	4	7	21	46	20

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	Artificial Intelligence Review	15
2	Expert Systems with Applications	15
3	Computers & Security	10
4	Neural Computing and Applications	9
5	Knowledge-Based Systems	9
6	Journal Of Big Data	9
7	Information Sciences	6
8	Cognitive Computation	3
9	Journal of Industrial Information Integration	3
10	Knowledge and Information Systems	2

Institutions with the most papers, since 2023
#	Institution	Papers
1	UNSW Sydney	4
2	Beijing University of Posts and Telecommunications	3
3	The University of Sydney	3
4	UNSW Canberra	3
5	Ajman University	2
6	Beihang University	2
7	Dalian University of Technology	2
8	Deakin University	2
9	Edinburgh Napier University	2
10	King Saud University	2

Countries with the most papers, since 2023
#	Country	Papers
1	China	22
2	India	15
3	United Kingdom	12
4	Saudi Arabia	12
5	United States	11
6	Australia	10
7	United Arab Emirates	5
8	Egypt	5
9	Pakistan	5
10	Algeria	4

Keywords with the most papers, since 2023
#	Keyword	Papers
1	Internet of Things security	36
2	intrusion detection	29
3	ToN-IoT	25
4	feature selection	17
5	class imbalance	15
6	intrusion detection systems	15
7	network traffic classification	14
8	federated learning	12
9	CICIDS2017	11
10	UNSW-NB15	11

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	315	9.7	Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction	Journal Of Big Data	2024-02-22	Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin et al.
2	200	6.2	Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning	Journal Of Big Data	2024-02-24	Jing Li, Mohd Shahizan Othman, Hewan Chen et al.
3	199	5.2	A stacking ensemble of deep learning models for IoT intrusion detection	Knowledge-Based Systems	2023-09-01	Riccardo Lazzarini, Huaglory Tianfield, Vassilis Charissis
4	182	10.6	Transformers and large language models for efficient intrusion detection systems: A comprehensive survey	Information Fusion	2025-05-29	Hamza Kheddar
5	144	4.3	Dugat-LSTM: Deep learning based network intrusion detection system using chaotic optimization strategy	Expert Systems with Applications	2024-01-11	Ramkumar Devendiran, Anil Vithalrao Turukmane

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Used together with#

Papers use IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) most often with CIC-IDS2017 / CSE-CIC-IDS2018 (43), NSL-KDD / KDD Cup 99 (36) and UNSW-NB15 (36); it has 8 partners in the code and data network, in the group around NSL-KDD / KDD Cup 99, UNSW-NB15, CIC-IDS2017 /…. The whole network →

What papers use IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
CIC-IDS2017 / CSE-CIC-IDS2018	Dataset	43	143.0
NSL-KDD / KDD Cup 99	Dataset	36	72.8
UNSW-NB15	Dataset	36	106.2
CIFAR-10 / CIFAR-100	Dataset	8	2.7
MNIST / Fashion-MNIST	Dataset	8	3.4
Kaggle	Dataset	6	4.9
ImageNet	Dataset	3	0.6
MODIS	Dataset	3	3.5

Cite this page

Scholar4AI. (2026). IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/iot-intrusion-data-bot-iot-ton-iot-ciciot2023

@misc{scholar4ai-datasets-iot-intrusion-data-bot-iot-ton-iot-ciciot2023,
  author = {{Scholar4AI}},
  title = {IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/iot-intrusion-data-bot-iot-ton-iot-ciciot2023}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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