LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection
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Network intrusion detection is one of the main problems in ensuring the security of modern computer networks, Wireless Sensor Networks (WSN), and the Internet-of-Things (IoT). In order to develop efficient network-intrusion-detection methods, realistic and up-to-date network flow datasets are required. Despite several recent efforts, there is still a lack of real-world network-based datasets which can capture modern network traffic cases and provide examples of many different types of network attacks and intrusions. To alleviate this need, we present LITNET-2020, a new annotated network benchmark dataset obtained from the real-world academic network. The dataset presents real-world examples of normal and under-attack network traffic. We describe and analyze 85 network flow features of the dataset and 12 attack types. We present the analysis of the dataset features by using statistical analysis and clustering methods. Our results show that the proposed feature set can be effectively used to identify different attack classes in the dataset. The presented network dataset is made freely available for research purposes.
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These deep sequence and generative models can be sensitive to noisy or irregular traffic, missing packets, and inaccurate labels, which may lead to degraded performance when real-world traffic deviates from the structured patterns seen during training.
“However, these models can be sensitive to noisy or irregular traffic, missing packets, and inaccurate labels, which may lead to degraded performance when real-world traffic deviates from the structured patterns seen during training [35,36,41].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [36]
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- Authors
- Damasevicius, R., Venckauskas, A., Grigaliunas, S., Toldinas, J., Morkevicius, N., Aleliunas, T., & Smuikys, P.
- Affiliations
- Faculty of Informatics, Kaunas University of Technology, 51386 Kaunas, Lithuania
- Kaunas University of Technology (LT)
- ORCID
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-05-13
- Volume
- 9
- Issue
- 5
- Pages
- 800
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3024012711
- Cited by
- 132
- References
- 78
- Citations by year
- 2026: 9 · 2025: 25 · 2024: 18 · 2023: 25 · 2022: 28 · 2021: 25 · 2020: 2
- FWCI
- 9.40
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- KTUePubl (Repository of Kaunas University of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (830892)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network intrusion detection, network traffic classification, network flow features, feature analysis, wireless sensor networks, Internet of Things security
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