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Network Anomaly Detection covers wide area of research. Current best practices for identifying and diagnosing traffic anomalies consist of visualizing traffic from different perspectives and identifying anomalies from prior experience. Different tools have been developed to automatically generate alerts to failures, but to automate the anomaly identification process remains a challenge. Recently, Signal Processing techniques have found applications in Network Intrusion Detection System because of their ability in detecting novel intrusions and attacks, which cannot be achieved by signature-based detection systems. Visualization techniques are ways of creating and handling graphical representations of data. This survey explains the main techniques known in the field of Statistical based and Wavelet based anomaly detection approaches and focuses on the role of data traffic visualization tools in network traffic anomaly detection.