지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 325 - 334 (10page)
- DOI
- 10.33162/JAR.2023.12.23.4.325
이용수
초록· 키워드
오류제보하기
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.
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.
목차
1. 서론
2. 이론적 배경
3. 제안 방법론
4. 사례연구
5. 결론 및 향후연구
References
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