Published August 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

A Review of Anomaly Detection Machine Learning and Deep Learning Techniques

Creators

  • 1. Student, Department of Computer Science, IES, IPS Academy Indore (M.P.), India.

Contributors

Contact person:

Researcher:

  • 1. Student, Department of Computer Science, IES, IPS Academy Indore (M.P.), India.
  • 2. Assistant Professor, Department of Computer Science, IES, IPS Academy Indore (M.P.), India.

Description

Abstract: In general, an anomaly may be defined as a variance or departure from what is expected or normal. According to historical records, the term "anomaly" was derived from the Greek word "anomalia," which translates as "uneven" or "irregular." Many examples of such abnormalities or deviations from normalcy have occurred in our everyday lives, and we have all seen them. For example, when a condition monitoring system detects any value or parameter of the machine that falls outside the minimum value limit to the maximum value limit, it beeps an alert. Similarly, when credit card fraud is detected, it notifies both the bank and the customer immediately. Currently, the most challenging problem is determining how to identify irregularities in data streams. An example of a data stream is a continuous stream of data that is continuously formed from any source and is referred to as streaming data (also known as streaming information). Finding anomalies in such a significant amount of data will be a time-consuming and challenging endeavour. In this paper, we will delve into the details of data streams and anomaly detection in data streams, examining a substantial number of papers and articles published on the topic in recent years.

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Additional details

Identifiers

DOI
10.35940/ijese.E2528.13090825
EISSN
2319-6378

Dates

Accepted
2025-08-15
Manuscript received on 26 June 2025 | First Revised Manuscript received on 12 July 2025 | Second Revised Manuscript received on 22 July 2025 | Manuscript Accepted on 15 August 2025 | Manuscript published on 30 August 2025.

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