에너지 특성 기반 통신 네트워크 신호 데이터의 이상진단
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이재승 | - |
dc.contributor.author | 임문원 | - |
dc.contributor.author | 배석주 | - |
dc.date.accessioned | 2024-01-11T02:30:33Z | - |
dc.date.available | 2024-01-11T02:30:33Z | - |
dc.date.issued | 2023-12 | - |
dc.identifier.issn | 1738-9895 | - |
dc.identifier.issn | 2733-8320 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/194362 | - |
dc.description.abstract | Purpose: With the rapid development of wireless communication, the occurrence of anomalies resulting from malicious network attacks and system overload is also increasing rapidly. Consequently, detecting network traffic anomalies in a network system has become crucial for preventing server downtime. In this study, we proposed a method for detecting anomalies in network traffic data through signal processing and statistical tests. Methods: Based on self-similar characteristics of network traffic data, we employed fractional Brownian motion to extract the Hurst exponent as the health index of network traffic data. Additionally, we proposed the index-based change-point monitoring scheme to assess the network’s current status. Results: Analysis of actual network traffic data shows that the method based on the Hurst exponent and change-point estimation can effectively detect anomalies early, prior to real traffic outbreak, preventing network traffic failures. Conclusion: This research introduced a method for assessing abnormalities and detecting change-point in network overload based on the statistical property of long-range dependency, facilitating early detection of network issues. | - |
dc.format.extent | 10 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 한국신뢰성학회 | - |
dc.title | 에너지 특성 기반 통신 네트워크 신호 데이터의 이상진단 | - |
dc.title.alternative | Energy Feature-based Anomaly Detection for Network Traffic Signal Data | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.33162/JAR.2023.12.23.4.325 | - |
dc.identifier.bibliographicCitation | 신뢰성 응용연구, v.23, no.4, pp 325 - 334 | - |
dc.citation.title | 신뢰성 응용연구 | - |
dc.citation.volume | 23 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 325 | - |
dc.citation.endPage | 334 | - |
dc.identifier.kciid | ART003025252 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Change-point | - |
dc.subject.keywordAuthor | Condition Monitoring | - |
dc.subject.keywordAuthor | Fractional Brownian Motion | - |
dc.subject.keywordAuthor | Hurst Exponent | - |
dc.subject.keywordAuthor | Two-Sample t-Test | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11643040&language=ko_KR&hasTopBanner=true | - |
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