InSDN: A Novel SDN Intrusion Dataset
|Title:||InSDN: A Novel SDN Intrusion Dataset||Authors:||Elsayed, Mahmoud Said; Le-Khac, Nhien-An; Jurcut, Anca Delia||Permanent link:||http://hdl.handle.net/10197/12615||Date:||8-Sep-2020||Online since:||2021-11-10T10:32:07Z||Abstract:||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 The majority of the published studies use non-compatible and outdated datasets, such as the KDD’99 dataset.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.||Type of material:||Journal Article||Publisher:||IEEE||Journal:||IEEE Access||Volume:||8||Start page:||165263||End page:||165284||Copyright (published version):||2020 the Authors||Keywords:||Datasets; Intrusion detection system (IDS); OpenFlow; SDN; Security; Threat vectors; Machine learning||DOI:||10.1109/access.2020.3022633||Language:||en||Status of Item:||Peer reviewed||ISSN:||2169-3536||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
Show full item record
If you are a publisher or author and have copyright concerns for any item, please email email@example.com and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.