Title: Characterisation of anomaly detection algorithms using simulated dataset for algorithm selection in application cases
Authors: D. Divya; M. Bhasi; M.B. Santosh Kumar
Addresses: Division of IT, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, 682022, India ' School of Management Studies, Cochin University of Science and Technology, Kochi, Kerala, 682022, India ' Division of IT, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, 682022, India
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.
Keywords: anomaly detection; data statistics; anomaly statistics; benchmark; characterisation; data generation; simulation; application framework; case study; recommendation system; detection rate.
International Journal of Embedded Systems, 2022 Vol.15 No.2, pp.93 - 107
Received: 25 Apr 2021
Accepted: 13 Aug 2021
Published online: 08 Jun 2022 *