Characterisation of anomaly detection algorithms using simulated dataset for algorithm selection in application cases Online publication date: Wed, 08-Jun-2022
by D. Divya; M. Bhasi; M.B. Santosh Kumar
International Journal of Embedded Systems (IJES), Vol. 15, No. 2, 2022
Abstract: Analysing the suitability of a particular anomaly detection algorithm in a real-time scenario is a challenging task since the benchmark studies are either restricted to a specific application or limited to characterisation study of outliers from a statistical point of view. This study makes an effort to design a system that performs characterisation study of anomaly detection algorithms based on data and anomaly statistics. Comparison of different anomaly detection algorithms was done by conducting an experimental study on a generated dataset that follows various data distributions and has distinct types and percentages of anomalies. Using this simulation model an application framework is proposed which is applicable to multiple industrial domains. Validation of the system is done using the case analysis that guides the users to utilise the recommender system for their application. Output of the recommender system suggests a suitable algorithm for their specific application.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Embedded Systems (IJES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook