Abstract
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.
Highlights
Network attacks are a set of network traffic events which are aimed at undermining the availability, authority, confidentiality, integrity, and other critical properties of networked computer systems [1].Various types of cyber-attacks, such as IP spoofing [2,3] and Distributed Denial-of-Service (DDoS)flooding attacks [4], have been recognized as a serious security problem
The time slice connection can be embedded to lower to dimensional space, where clustering methodsmethods can be applied map the features can be embedded lower dimensional space, where clustering can beto applied to low-dimensional representation of features to network attack classes
The proposed network dataset was collected for a longer period of time (10 months) than
Summary
Network attacks are a set of network traffic events which are aimed at undermining the availability, authority, confidentiality, integrity, and other critical properties of networked computer systems [1]. The effectiveness of NIDS is evaluated based on their performance to recognize attacks, which requires a network dataset that provides examples of both normal and abnormal network traffic [36] Old benchmark datasets such as KDDCup’99 [37] and NSL-KDD [38] have been widely used for evaluating the accuracy of network-attack recognition [39,40,41,42]. Unsupervised methods can potentially recognize unknown attacks with no prior knowledge, on which the supervised methods (which require datasets with data labeled by attack type) fail miserably [11] To tackle these problems, Fadllulah et al [56] extracted features for detecting attacks against encrypted protocols and generated normal-usage behavior profiles.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
