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Detecting network traffic anomalies is crucial for network operators as it helps to identify security incidents and to supervise the availability of networked services. Although anomaly recognition has received significant attention in the literature, the automatic classification of network anomalies still remains an open problem. Network administration and security is now one of the most energetic examines areas, among which, research on perceive and identifying anomalies has attracted a lot of interest. Researchers are still worried to find a useful and lightweight method for anomaly recognition purpose.
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