A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability

A. S. Manjunatha; Venkatramana P. Bhat; M. Ramakrishna; Ananya

A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability

A. S. Manjunatha
, Venkatramana P. Bhat
, M. Ramakrishna
, Ananya

School of Computer Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

Wireless Sensor Network plays an important role in many fields ranging from healthcare to industrial automation, which require robust anomaly detection techniques to prevent damage that might occur to the collected data. This work exploits various Machine Learning algorithms to detect anomalies in WSN and helps one to maintain integrity of the data collected. It is very important to maintain the integrity of the data collected through Wireless Sensor Networks and this paper is envisioned to present an effective anomaly detection mechanism to improve the reliability of WSNs.

Original language	English
Pages	12448-12452
Number of pages	5
Publication status	Published - 2025
Event	16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025 - Hyderabad, India Duration: 25-06-2025 → 26-06-2025

Conference

Conference	16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025
Country/Territory	India
City	Hyderabad
Period	25-06-25 → 26-06-25

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Control and Systems Engineering
Industrial and Manufacturing Engineering

Cite this

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title = "A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability",

abstract = "Wireless Sensor Network plays an important role in many fields ranging from healthcare to industrial automation, which require robust anomaly detection techniques to prevent damage that might occur to the collected data. This work exploits various Machine Learning algorithms to detect anomalies in WSN and helps one to maintain integrity of the data collected. It is very important to maintain the integrity of the data collected through Wireless Sensor Networks and this paper is envisioned to present an effective anomaly detection mechanism to improve the reliability of WSNs.",

author = "Manjunatha, \{A. S.\} and Bhat, \{Venkatramana P.\} and M. Ramakrishna and Ananya",

note = "Publisher Copyright: {\textcopyright} Grenze Scientific Society, 2025.; 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025 ; Conference date: 25-06-2025 Through 26-06-2025",

year = "2025",

language = "English",

pages = "12448--12452",

}

Manjunatha, AS, Bhat, VP, Ramakrishna, M & Ananya 2025, 'A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability', Paper presented at 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025, Hyderabad, India, 25-06-25 - 26-06-25 pp. 12448-12452.

A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability. / Manjunatha, A. S.; Bhat, Venkatramana P.; Ramakrishna, M. et al.
2025. 12448-12452 Paper presented at 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025, Hyderabad, India.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks

T2 - 16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025

AU - Manjunatha, A. S.

AU - Bhat, Venkatramana P.

AU - Ramakrishna, M.

AU - Ananya,

PY - 2025

Y1 - 2025

N2 - Wireless Sensor Network plays an important role in many fields ranging from healthcare to industrial automation, which require robust anomaly detection techniques to prevent damage that might occur to the collected data. This work exploits various Machine Learning algorithms to detect anomalies in WSN and helps one to maintain integrity of the data collected. It is very important to maintain the integrity of the data collected through Wireless Sensor Networks and this paper is envisioned to present an effective anomaly detection mechanism to improve the reliability of WSNs.

AB - Wireless Sensor Network plays an important role in many fields ranging from healthcare to industrial automation, which require robust anomaly detection techniques to prevent damage that might occur to the collected data. This work exploits various Machine Learning algorithms to detect anomalies in WSN and helps one to maintain integrity of the data collected. It is very important to maintain the integrity of the data collected through Wireless Sensor Networks and this paper is envisioned to present an effective anomaly detection mechanism to improve the reliability of WSNs.

UR - https://www.scopus.com/pages/publications/105034901757

UR - https://www.scopus.com/pages/publications/105034901757#tab=citedBy

M3 - Paper

AN - SCOPUS:105034901757

SP - 12448

EP - 12452

Y2 - 25 June 2025 through 26 June 2025

ER -

A Study on Machine Learning Techniques for Anomaly Detection in Wireless Sensor Networks: Enhancing Data Integrity and Reliability

Abstract

Conference

All Science Journal Classification (ASJC) codes

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