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Detecting Anomalies in Irregular Data Using K-means Clustered Signal Dictionary
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作者 G. Talavera Reyes rajan m. chandra +1 位作者 Ha Thu Le Zekeriya Aliyazicioglu 《Computer Technology and Application》 2016年第5期244-252,共9页
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 exh... 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 fi'om 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. 展开更多
关键词 Intrusion detection irregular data K-means clustering machine learning signal dictionary
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