A new histogram-based approach for visualizing anomaly detection algorithm performance and prediction confidence.

Background
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Performance visualization of anomaly detection algorithms is an 
essential aspect of anomaly and intrusion detection systems. 
It allows analysts to highlight trends and outliers in anomaly 
detection models results to gain intuitive understanding of detection
models. This work presents a new way of visualizing anomaly 
detection algorithm results using a histogram. 

Importance of the Visualization Approach
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- provides a better understanding
of detection algorithms performance by revealing the exact 
proportions of true positives, true negatives, false positives, 
and false negatives of detection algorithms. 
- provides insights into the strengths and weaknesses of detection
algorithms performance on different aspects of a datasets unlike 
previous approaches that rely on only positive and negative 
decision scores. 
- can be applied to performance visualization and analysis of 
supervised machine learning techniques involving 
binary classification of imbalanced datasets.


References
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Users can refer to our paper below for further insight and 
examples:
- Aboah Boateng E, Bruce JW. Unsupervised Machine Learning 
Techniques for Detecting PLC Process Control Anomalies. Journal of Cybersecurity and Privacy. 
2022; 2(2):220-244. https://doi.org/10.3390/jcp2020012

