  In this era of big data, databases are growing rapidly in terms of the number
of records. Fast automatic detection of anomalous records in these massive
databases is a challenging task. Traditional distance based anomaly detectors
are not applicable in these massive datasets. Recently, a simple but extremely
fast anomaly detector using one-dimensional histograms has been introduced. The
anomaly score of a data instance is computed as the product of the probability
mass of histograms in each dimensions where it falls into. It is shown to
produce competitive results compared to many state-of-the-art methods in many
datasets. Because it assumes data features are independent of each other, it
results in poor detection accuracy when there is correlation between features.
To address this issue, we propose to increase the feature size by adding more
features based on principal components. Our results show that using the
original input features together with principal components improves the
detection accuracy of histogram-based anomaly detector significantly without
compromising much in terms of run-time.
