---
title: "Cybersecurity and anomaly detection benchmarks datasets"
description: "Datasets in the category “Cybersecurity and anomaly detection benchmarks” were mentioned in 6 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #45 of the 58 data categories mentioned."
url: https://scholar4ai.com/datasets/cybersecurity-and-anomaly-detection-benchmarks
markdown_url: https://scholar4ai.com/datasets/cybersecurity-and-anomaly-detection-benchmarks.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Datasets in the category “Cybersecurity and anomaly detection benchmarks” were mentioned in 6 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #45 of the 58 data categories mentioned."
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T14:52:47+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Cybersecurity and anomaly detection benchmarks

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

- 0 papers in the last 7 days
- 5 of its 6 datasets mentioned

Back to the ranking: https://scholar4ai.com/rankings/datasets.md

## Key findings

- Datasets in the category “Cybersecurity and anomaly detection benchmarks” were mentioned in 6 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #45 of the 58 data categories mentioned.
- The most-mentioned were UNSW-NB15 (4), CIC-IDS2017 / CSE-CIC-IDS2018 (2), NSL-KDD / KDD Cup 99 (2), Credit card fraud data (Kaggle, PaySim, IEEE-CIS) (1) and Industrial control anomalies (SWaT, WADI) (1).
- By journal category: AI (6).
- Most of them appeared in Neural Computing and Applications (3), Pattern Recognition (1) and Machine Learning (1).

## Datasets

*Datasets in this category · 2026-09-27 – 2026-10-26 (30 days)*

| Dataset | Papers | Last 7 days | Names searched for |
|---|---:|---:|---|
| UNSW-NB15 | 4 | 0 | UNSW-NB15 |
| CIC-IDS2017 / CSE-CIC-IDS2018 | 2 | 0 | CIC-IDS2017, CICIDS2017, CIC-IDS-2017, CSE-CIC-IDS2018, CIC-IDS2018, CICIDS2018 |
| NSL-KDD / KDD Cup 99 | 2 | 0 | NSL-KDD, KDD Cup 99, KDD99, KDDCup99, KDD Cup 1999 |
| Credit card fraud data (Kaggle, PaySim, IEEE-CIS) | 1 | 0 | credit card fraud detection dataset, European cardholders, PaySim, IEEE-CIS |
| Industrial control anomalies (SWaT, WADI) | 1 | 0 | WADI, SWaT |
| IoT intrusion data (Bot-IoT, TON_IoT, CICIoT2023) | 0 | 0 | Bot-IoT, TON_IoT, ToN-IoT, CICIoT2023, Edge-IIoTset |

## Papers

*Papers mentioning these datasets, newest first.*

| Date | Title | Journal | Authors | Affiliations | Datasets mentioned |
|---|---|---|---|---|---|
| 2026-10-09 | [Reliability-aware dynamic dependency graph neural network for airborne network intrusion detection](https://doi.org/10.1016/j.patcog.2026.115103) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Wenqi Liu, Maohui Zhou, Cong Gao et al. | Nanjing University of Aeronautics and Astronautics | UNSW-NB15, CIC-IDS2017 / CSE-CIC-IDS2018 |
| 2026-10-01 | [A domain-aware explainable AI framework for multi-class intrusion detection with cybersecurity-oriented semantic interpretation](https://doi.org/10.1007/s00521-026-12597-9) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Salah ElDin Zaher Olaymi | Taibah University | UNSW-NB15, CIC-IDS2017 / CSE-CIC-IDS2018 |
| 2026-10-01 | [A comprehensive empirical evaluation of quantum machine learning for financial prediction: systematic comparison with classical approaches](https://doi.org/10.1007/s00521-026-12516-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Raed Abu Zitar, Samia Loucif, Murad M. Al-Rajab et al. | Liwa University; Zayed University; Abu Dhabi University; … | Credit card fraud data (Kaggle, PaySim, IEEE-CIS) |
| 2026-10-01 | [A Synergistic approach: blockchain-integrated machine learning based intrusion detection system for improving wireless network security](https://doi.org/10.1007/s00521-026-12503-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Walaa M. Elsayed | Damanhour University | NSL-KDD / KDD Cup 99, UNSW-NB15 |
| 2026-10-01 | [Addressing Methodological Challenges in Time Series Anomaly Detection](https://doi.org/10.1007/s10994-026-07183-y) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Nancy Xu, Henrik Boström |  | Industrial control anomalies (SWaT, WADI) |
| 2026-10-01 | [Multi-view self-supervised learning for label-flipping robustness in tabular data: a comparative study](https://doi.org/10.1007/s10489-026-07452-2) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Jinhyeok Jang, Sohee Park, Seung-Soo Nam et al. | Soongsil University | NSL-KDD / KDD Cup 99, UNSW-NB15 |

## Cite this page

Scholar4AI. (2026). Cybersecurity and anomaly detection benchmarks. Journal Research Rankings. Retrieved October 26, 2026, from https://scholar4ai.com/datasets/cybersecurity-and-anomaly-detection-benchmarks

```bibtex
@misc{scholar4ai-datasets-cybersecurity-and-anomaly-detection-benchmarks,
  author = {{Scholar4AI}},
  title = {Cybersecurity and anomaly detection benchmarks},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/cybersecurity-and-anomaly-detection-benchmarks}},
  urldate = {2026-10-26},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).