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In this era of big data, databases are growing rapidly in terms of the numberof records. Fast automatic detection of anomalous records in these massivedatabases is a challenging task. Traditional distance based anomaly detectorsare not applicable in these massive datasets. Recently, a simple but extremelyfast anomaly detector using one-dimensional histograms has been introduced. Theanomaly score of a data instance is computed as the product of the probabilitymass of histograms in each dimensions where it falls into. It is shown toproduce competitive results compared to many state-of-the-art methods in manydatasets. Because it assumes data features are independent of each other, itresults in poor detection accuracy when there is correlation between features.To address this issue, we propose to increase the feature size by adding morefeatures based on principal components. Our results show that using theoriginal input features together with principal components improves thedetection accuracy of histogram-based anomaly detector significantly withoutcompromising much in terms of run-time.

Improved histogram-based anomaly detector with the extended principal component features