DDoS detection using CURE clustering algorithm with outlier removal clustering for handling outliers

DoS (Denial of Service) and DDoS (Distributed Denial of Service) is an anomalous traffic phenomena that is need serious attention. In the previous research has already been discussed on traffic anomaly detection based on clustering, with a hierarchical clustering algorithm method. In this paper, we...

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Bibliographic Details
Published in2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC) pp. 12 - 18
Main Authors Laksono, Muhammad Agung Tri, Purwanto, Yudha, Novianty, Astri
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Algorithm design and analysis
Clustering algorithms
Computer crime
CURE clustering algorithm
DDoS
Intrusion detection
outliers
Partitioning algorithms
Shape
Telecommunication traffic
traffic anomaly detection
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Summary:DoS (Denial of Service) and DDoS (Distributed Denial of Service) is an anomalous traffic phenomena that is need serious attention. In the previous research has already been discussed on traffic anomaly detection based on clustering, with a hierarchical clustering algorithm method. In this paper, we introduce a method of network traffic anomaly (DDoS) detection using modernization of the traditional hierarchical clustering algorithm that is CURE clustering algorithm. CURE has advantages in the case of outliers. We modify the algorithm using outlier removal clustering (ORC) in terms of dealing with outliers. We apply the mechanism to detect and remove outliers from the specified clusters. We perform the outlier elimination scheme in two phase and do the removal at the point which detected as outlier. We also give an analysis and results of the proposed method.
DOI:10.1109/ICCEREC.2015.7337029