Anomaly Detection for Highly Imbalanced Data-an Empirical Analysis

Akshat Ajay Das, Veena Mayya, Manohara M.M. Pai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An event or an observation that is statistically different from the others is termed an anomaly. Anomaly detection is the process of identifying such anomalies. Anomaly detection is an effective tool for risk mitigation, fraud detection, and improving the system's robustness. It is also an active research area, with numerous algorithms being proposed. In this paper, we compare the performance of various anomaly detection algorithms on mul-tivariate as well as univariate datasets. The assessment measures generated are important and can be beneficial for predicting anomalies in a timely and accurate manner. Experimental results demonstrate that on a univariate dataset, the auto-regressive moving average (ARMA), performs better than the local outlier factor (LOF), while on a multivariate dataset, the LOF model performs better. The prototype developed has been extensively tested on publicly available datasets and can be evaluated on larger, more comprehensive datasets for deployment in the real-time anomaly detection setup.

Original languageEnglish
Title of host publication2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665475242
DOIs
Publication statusPublished - 2023
Event5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023 - Pune, India
Duration: 01-03-202303-03-2023

Publication series

Name2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023

Conference

Conference5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023
Country/TerritoryIndia
CityPune
Period01-03-2303-03-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Safety, Risk, Reliability and Quality

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