An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zainudin, Ahmad | - |
dc.contributor.author | Ahakonye, Love Allen Chijioke | - |
dc.contributor.author | Akter, Rubina | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.contributor.author | Lee, Jae-Min | - |
dc.date.accessioned | 2023-08-31T12:40:09Z | - |
dc.date.available | 2023-08-31T12:40:09Z | - |
dc.date.created | 2023-07-05 | - |
dc.date.issued | 2023-05 | - |
dc.identifier.issn | 2327-4662 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21762 | - |
dc.description.abstract | Software-defined networking (SDN)-based Industrial Internet of Things (IIoT) networks have a centralized controller that is a single attractive target for unauthorized users to attack. Cybersecurity in IIoT networks is becoming the most significant challenge, especially from increasingly sophisticated Distributed Denial-of-Service (DDoS) attacks. This situation necessitates efficient approaches to mitigate recent attacks following the incompetence of existing techniques that focus more on DDoS detection. Most existing DDoS detection capabilities are computationally complex and are no longer efficient enough to protect against DDoS attacks. Thus, the need for a low-cost approach for DDoS attack classification. This study presents a competent feature selection method extreme gradient boosting (XGBoost) for determining the most relevant data features with a hybrid convolutional neural network and long short-term memory (CNN-LSTM) for DDoS attack classification. The proposed model evaluated the CICDDoS2019 data set with improved accuracy and low-complexity capability for low latency IIoT requirements. Performance results show that the proposed model achieves a high accuracy of 99.50% with a time cost of 0.179 ms. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Zainudin, Ahmad | - |
dc.contributor.affiliatedAuthor | Ahakonye, Love Allen Chijioke | - |
dc.contributor.affiliatedAuthor | Akter, Rubina | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Seong | - |
dc.contributor.affiliatedAuthor | Lee, Jae-Min | - |
dc.identifier.doi | 10.1109/JIOT.2022.3196942 | - |
dc.identifier.scopusid | 2-s2.0-85136149470 | - |
dc.identifier.wosid | 000982455700014 | - |
dc.identifier.bibliographicCitation | IEEE INTERNET OF THINGS JOURNAL, v.10, no.10, pp.8491 - 8504 | - |
dc.relation.isPartOf | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.title | IEEE INTERNET OF THINGS JOURNAL | - |
dc.citation.volume | 10 | - |
dc.citation.number | 10 | - |
dc.citation.startPage | 8491 | - |
dc.citation.endPage | 8504 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | INTRUSION DETECTION | - |
dc.subject.keywordPlus | INDUSTRIAL INTERNET | - |
dc.subject.keywordPlus | ATTACK DETECTION | - |
dc.subject.keywordPlus | MECHANISM | - |
dc.subject.keywordPlus | MACHINE | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Industrial Internet of Things | - |
dc.subject.keywordAuthor | Computer crime | - |
dc.subject.keywordAuthor | Denial-of-service attack | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Floods | - |
dc.subject.keywordAuthor | Low latency communication | - |
dc.subject.keywordAuthor | Telecommunication traffic | - |
dc.subject.keywordAuthor | Convolutional neural network and long short-term memory (CNN-LSTM) | - |
dc.subject.keywordAuthor | Distributed Denial-of-Service (DDoS) detection and classification | - |
dc.subject.keywordAuthor | feature selection (FS) | - |
dc.subject.keywordAuthor | Industrial Internet of Things (IIoT) | - |
dc.subject.keywordAuthor | software-defined networking (SDN) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.