TY - GEN
T1 - Anomaly Detection for Highly Imbalanced Data-an Empirical Analysis
AU - Das, Akshat Ajay
AU - Mayya, Veena
AU - Pai, Manohara M.M.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85158164146&partnerID=8YFLogxK
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U2 - 10.1109/ESCI56872.2023.10100135
DO - 10.1109/ESCI56872.2023.10100135
M3 - Conference contribution
AN - SCOPUS:85158164146
T3 - 2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023
BT - 2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023
Y2 - 1 March 2023 through 3 March 2023
ER -