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The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers. Detection of outliers can lead to<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/comjnl/bxr026">doi:10.1093/comjnl/bxr026</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/smdimqftezaufcdgcebjz5exmq">fatcat:smdimqftezaufcdgcebjz5exmq</a> </span>
more »... of system faults so that administrators can take preventive measures before they escalate. It is possible that anomaly detection may enable detection of new attacks. Outlier detection is an important anomaly detection approach. In this paper, we present a comprehensive survey of well known distance-based, density-based and other techniques for outlier detection and compare them. We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.
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