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Identifying network anomalies is essential in enterprise and provider networks for diagnosing events, like attacks or failures, that severely impact performance, security, and Service Level Agreements (SLAs). Feature-based anomaly detection models (ab)normal network traffic behavior by analyzing different packet header features, like IP addresses and port numbers. In this work, we describe a new approach to feature-based anomaly detection that constructs histograms of different traffic features, models histogram patterns, and identifies deviations from the created models. We assess the strengths and weaknesses of many design options, like the utility of different features, the construction of feature histograms, the modeling and clustering algorithms, and the detection of deviations. Compared to previous feature-based anomaly detection approaches, our work differs by constructing detailed histogram models, rather than using coarse entropy-based distribution approximations. We evaluate histogram-based anomaly detection and compare it to previous approaches using collected network traffic traces. Our results demonstrate the effectiveness of our technique in identifying a wide range of anomalies. The assessed technical details are generic and, therefore, we expect that the derived insights will be useful for similar future research efforts.