ECSD: Enhanced Compromised Switch Detection in an SDN-Based Cloud through Multivariate Time-Series Analysis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Dinh, P.T. | - |
dc.contributor.author | Park, M. | - |
dc.date.available | 2020-09-09T06:05:09Z | - |
dc.date.created | 2020-09-05 | - |
dc.date.issued | 2020-06 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/38604 | - |
dc.description.abstract | Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall; however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | ECSD: Enhanced Compromised Switch Detection in an SDN-Based Cloud through Multivariate Time-Series Analysis | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3004258 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.119346 - 119360 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000551985700001 | - |
dc.identifier.scopusid | 2-s2.0-85088113454 | - |
dc.citation.endPage | 119360 | - |
dc.citation.startPage | 119346 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Park, M. | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | distributed cloud computing | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | network function virtualization | - |
dc.subject.keywordAuthor | Network security | - |
dc.subject.keywordAuthor | SDN compromised switch | - |
dc.subject.keywordAuthor | software defined networking | - |
dc.subject.keywordPlus | Anomaly detection | - |
dc.subject.keywordPlus | Recurrent neural networks | - |
dc.subject.keywordPlus | Stochastic systems | - |
dc.subject.keywordPlus | Autoregressive integrated moving average models | - |
dc.subject.keywordPlus | Efficient schemes | - |
dc.subject.keywordPlus | Evaluation results | - |
dc.subject.keywordPlus | Multivariate time series | - |
dc.subject.keywordPlus | Multivariate time series analysis | - |
dc.subject.keywordPlus | Network behaviors | - |
dc.subject.keywordPlus | Stochastic recurrent neural network | - |
dc.subject.keywordPlus | Stochastic variable | - |
dc.subject.keywordPlus | Time series analysis | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL 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.