  The visualization and detection of anomalies (outliers) are of crucial
importance to many fields, particularly cybersecurity. Several approaches have
been proposed in these fields, yet to the best of our knowledge, none of them
has fulfilled both objectives, simultaneously or cooperatively, in one coherent
framework. The visualization methods of these approaches were introduced for
explaining the output of a detection algorithm, not for data exploration that
facilitates a standalone visual detection. This is our point of departure:
UN-AVOIDS, an unsupervised and nonparametric approach for both visualization (a
human process) and detection (an algorithmic process) of outliers, that assigns
invariant anomalous scores (normalized to $[0,1]$), rather than hard
binary-decision. The main aspect of novelty of UN-AVOIDS is that it transforms
data into a new space, which is introduced in this paper as neighborhood
cumulative density function (NCDF), in which both visualization and detection
are carried out. In this space, outliers are remarkably visually
distinguishable, and therefore the anomaly scores assigned by the detection
algorithm achieved a high area under the ROC curve (AUC). We assessed UN-AVOIDS
on both simulated and two recently published cybersecurity datasets, and
compared it to three of the most successful anomaly detection methods: LOF, IF,
and FABOD. In terms of AUC, UN-AVOIDS was almost an overall winner. The article
concludes by providing a preview of new theoretical and practical avenues for
UN-AVOIDS. Among them is designing a visualization aided anomaly detection
(VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection
algorithm (running in a back engine), NCDF visualization space (rendered to
plots), along with other conventional methods of visualization in the original
feature space, all of which are linked in one interactive environment.
