LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

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 requir...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2020-05, Vol.9 (5), p.800
Main Authors: Damasevicius, Robertas, Venckauskas, Algimantas, Grigaliunas, Sarunas, Toldinas, Jevgenijus, Morkevicius, Nerijus, Aleliunas, Tautvydas, Smuikys, Paulius
Format: Article
Language:English
Subjects:
Accuracy
Clustering
Communications traffic
Computer networks
Cybersecurity
Data encryption
Datasets
Floods
Internet of Things
Internet service providers
Intrusion detection systems
Medical equipment
Network security
Principal components analysis
Statistical analysis
Wireless networks
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics9050800