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Detecting Anomalies in Irregular Data Using K-means Clustered Signal Dictionary
G. Talavera Reyes, Rajan M. Chandra, Ha Thu Le and Zekeriya Aliyazicioglu
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DOI:10.17265/1934-7332/2016.05.003
Electrical and Computer Engineering Department, California State Polytechnic University Pomona, CA 91768, United States
The critical nature of satellite network traffic provides a challenging environment to detect intrusions. The intrusion detection method presented aims to raise an alert whenever satellite network signals begin to exhibit anomalous patterns determined by Euclidian distance metric. In line with anomaly-based intrusion detection systems, the method presented relies heavily on building a model of “normal” through the creation of a signal dictionary using windowing and k-means clustering. The results of three signals from our case study are discussed to highlight the benefits and drawbacks of the method presented. Our preliminary results demonstrate that the clustering technique used has great potential for intrusion detection for non-periodic satellite network signals.
Intrusion detection, irregular data, K-means clustering, machine learning, signal dictionary
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