  Monitoring network traffic data to detect any hidden patterns of anomalies is
a challenging and time-consuming task that requires high computing resources.
To this end, an appropriate summarization technique is of great importance,
where it can be a substitute for the original data. However, the summarized
data is under the threat of removing anomalies. Therefore, it is vital to
create a summary that can reflect the same pattern as the original data.
Therefore, in this paper, we propose an INtelligent Summarization approach for
IDENTifying hidden anomalies, called INSIDENT. The proposed approach guarantees
to keep the original data distribution in summarized data. Our approach is a
clustering-based algorithm that dynamically maps original feature space to a
new feature space by locally weighting features in each cluster. Therefore, in
new feature space, similar samples are closer, and consequently, outliers are
more detectable. Besides, selecting representatives based on cluster size keeps
the same distribution as the original data in summarized data. INSIDENT can be
used both as the preprocess approach before performing anomaly detection
algorithms and anomaly detection algorithm. The experimental results on
benchmark datasets prove a summary of the data can be a substitute for original
data in the anomaly detection task.
