For offline approaches ROC Curve and for online approaches AMOC curve

Hi Muhammed,

Here are some references on the subject :

- the authors present in the first paper a new geometric framework for unsupervised anomaly detector which are algorithms that are designed to process unlabled data.

- In the second paper the authors present the problem of detecting anomalies in time series data using Peer Group Analysis (PGA), which is an unsupervised technique.

- Finally, in the third paper the authors present the semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement.

1) A Geometric Framework for Unsupervised Anomaly Detection:Anomaly Detection Using Unsupervised Profiling

www.cs.cmu.edu/~aarnold/cald/uad-dmsa02.pdf

2) Anomaly Detection Using Unsupervised Profiling Method in Time Series Data

ceur-ws.org/Vol-215/paper06.pdf

3) Toward Supervised Anomaly Detection

https://www.jair.org/media/.../live-3623-6845-jair.pdf

Good Luck

Hi Muhammed,

Another reference that may interest you

Histogram-based OutlierScore(HbOS) : A fast Unsupervised Anomaly Detection Algorithm

WWW.dfki.de/k12012/.../ki2012pd13.pdf

Good Luck

Hi Muhammed,

For real time application, time response and accuracy (%, precision) are most important.

Good Luck

Hi Muhammed

1- For real time application, time response and accuracy .

2-For offline approaches( ROC )Curve and for online approaches( AMOC )curve.

Good Luck