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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61076
Volume:
10 |
4 |
April 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computing-Decision and Computing Sciences
Coimbatore Institute of Technology, Coimbatore, India
Department of Computing-Decision and Computing Sciences
Coimbatore Institute of Technology, Coimbatore, India
Anomaly detection is the identification of rare events, items, or observations which are suspicious because they differ significantly from standard behaviours or patterns. Anomalies in data are also called standard deviations, outliers, noise, novelties, and exceptions. In the network anomaly detection/network intrusion and abuse detection context, interesting events are often not rare. Anomaly detection is any process that finds the outliers of a dataset; those items that don’t belong. These anomalies might point to unusual network traffic, uncover a sensor on the fritz, or simply identify data for cleaning, before analysis. In today’s world of distributed systems, managing and monitoring the system’s performance is a chore—albeit a necessary chore. With hundreds or thousands of items to watch, anomaly detection can help point out where an error is occurring, enhancing root cause analysis and quickly getting tech support on the issue. Anomaly detection helps the monitoring cause of chaos engineering by detecting outliers, and informing the responsible parties to act.
KEYWORDS
The unsupervised anomaly detection methods work best when you’re not aware of the type of anomalies that may occur, especially with unstructured data. Supervised is best when sufficient data is available, and the nature of anomalies is consistent with the real world. According to the accuracy score Logistic regression works pretty well because predicting fraud transactions is a classification problem. So, this is one method to predict the fraud transaction but also there are many methods and algorithms are there to solve this problem. In this article, we covered what is anomaly detection and how can we use machine learning to perform such tasks
[1]. https://www.enjoyalgorithms.com/blog/introduction-to-anomaly-detection/ [2]. https://www.bmc.com/blogs/machine-learning-anomaly-detection/ [3]. https://www.projectpro.io/article/anomaly-detection-using-machine-learning-in-python-with-example/555 [4]. https://towardsdatascience.com/how-to-use-machine-learning-for-anomaly-detection-and-condition- monitoring-6742f82900d7 [5]. https://www.javatpoint.com/machine-learning-with-anomaly-detection
Publication Date
4/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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