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dc.contributor.author | Lent, Daniel M. Brandao![]() |
es_ES |
dc.contributor.author | Ruffo, Vitor G. da Silva![]() |
es_ES |
dc.contributor.author | Carvalho, Luiz F.![]() |
es_ES |
dc.contributor.author | Lloret, Jaime![]() |
es_ES |
dc.contributor.author | Rodrigues, Joel J. P. C.![]() |
es_ES |
dc.contributor.author | Proença Jr., Mario Lemes![]() |
es_ES |
dc.date.accessioned | 2024-07-19T18:05:58Z | |
dc.date.available | 2024-07-19T18:05:58Z | |
dc.date.issued | 2024 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/206446 | |
dc.description.abstract | [EN] Network management is a crucial task to maintain modern systems and applications running. Some applications have become vital for society and are expected to have zero downtime. Software-defined networks is a paradigm that collaborates with the scalability, modularity and manageability of systems by centralizing the network's controller. However, this creates a weak point for distributed denial of service attacks if unprepared. This study proposes an anomaly detection system to detect distributed denial of service attacks in software-defined networks using generative adversarial neural networks with gated recurrent units. The proposed system uses unsupervised learning to detect unknown attacks in an interval of 1 second. A mitigation algorithm is also proposed to stop distributed denial-of-service attacks from harming the network's operation. Two datasets were used to validate this model: the first developed by the computer networks study group Orion from the State University of Londrina. The second is a well-known dataset: CIC-DDoS2019, widely used by the anomaly detection community. Besides the gated recurrent units, other types of neurons are also tested in this work, they are: long short-term memory, convolutional and temporal convolutional. The detection module reached an F1-score of 99@ in the first dataset and 98@ in the second, while the mitigation module could drop 99@ of malicious flows in both datasets. | es_ES |
dc.description.sponsorship | This work was supported in part by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant 306397/2022-6 and Grant 306607/2023-9; in part by the Coordination for the Improvement of Higher Education Personnel Foundation of Brazil; and in part by the Superintendency of Science, Technology and Higher Education (SETI) and the State University of Londrina [Pró-Reitoria de Pesquisa e Pós-Graduação (PROPPG)]. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation.ispartof | IEEE Access | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Logic gates | es_ES |
dc.subject | Generators | es_ES |
dc.subject | Generative adversarial networks | es_ES |
dc.subject | Control systems | es_ES |
dc.subject | Neurons | es_ES |
dc.subject | Training | es_ES |
dc.subject | Biological neural networks | es_ES |
dc.subject | Anomaly detection | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Software defined networking | es_ES |
dc.subject | Software-defined networks | es_ES |
dc.subject.classification | INGENIERÍA TELEMÁTICA | es_ES |
dc.title | An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1109/ACCESS.2024.3402069 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CNPq//306607%2F2023-9/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CNPq//306397%2F2022-6/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Lent, DMB.; Ruffo, VGDS.; Carvalho, LF.; Lloret, J.; Rodrigues, JJPC.; Proença Jr., ML. (2024). An Unsupervised Generative Adversarial Network System to Detect DDoS Attacks in SDN. IEEE Access. 12:70690-70706. https://doi.org/10.1109/ACCESS.2024.3402069 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/ACCESS.2024.3402069 | es_ES |
dc.description.upvformatpinicio | 70690 | es_ES |
dc.description.upvformatpfin | 70706 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 12 | es_ES |
dc.identifier.eissn | 2169-3536 | es_ES |
dc.relation.pasarela | S\520394 | es_ES |
dc.contributor.funder | Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil | es_ES |