The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
InSDN: A Novel SDN Intrusion Dataset
Open accessAbstract
Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center's network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.
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Cites
Random forest in remote sensing: A review of applications and future directions
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A detailed analysis of the KDD CUP 99 data set
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Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318
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Deep Learning Approach for Intelligent Intrusion Detection System
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Toward developing a systematic approach to generate benchmark datasets for intrusion detection
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Cited by
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhang, Z., Hamadi, H. A., Damiani, E., Yeun, C. Y., & Taher, F. (2022) · IEEE Access · doi:10.1109/access.2022.3204051 · Cited by 450
Network Anomaly Detection Using LSTM Based Autoencoder
Said Elsayed, M., Le-Khac, N.-A., Dev, S., & Jurcut, A. D. (2020) · Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks · doi:10.1145/3416013.3426457 · Cited by 285
Self-citationDDoS attack detection and mitigation using deep neural network in SDN environment
Hnamte, V., Najar, A. A., Nhung-Nguyen, H., Hussain, J., & Sugali, M. N. (2024) · Computers & Security · doi:10.1016/j.cose.2023.103661 · Cited by 164
Detection of DDoS attacks in SDN-based VANET using optimized TabNet
Setitra, M. A., & Fan, M. (2024) · Computer Standards & Interfaces · doi:10.1016/j.csi.2024.103845 · Cited by 40
Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design
Sayin, B., Zoppi, T., Marchini, N., Khokhar, F. A., & Passerini, A. (2025) · IEEE Access · doi:10.1109/access.2025.3568501 · Cited by 7
“ADFANet [57], AndMal17 [58], CICIDS17 [59], CICIDS18 [59], CIDDS [60], IoT Network [61], ISCX12 [62], NSLKDD [63], SDN20 [64], UGR16 [65], and UNSW-NB15 [66] datasets contain data during normal network operation and during cyber-attacks.”
Metadata
- Authors
- Elsayed, M. S., Le-Khac, N.-A., & Jurcut, A. D.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 165263-165284
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3084346886
- Cited by
- 414
- References
- 65
- FWCI
- 17.44
- Type
- journal article
- Language
- en
- Open access
- gold
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