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January 1, 2026International Journal of Grid and Utility Computing

An efficient intrusion detection system using unsupervised learning AutoEncoder

N.D. Patel B.M. Mehtre Rajeev Wankar

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Key Points

The aim is to develop an efficient intrusion detection system using unsupervised learning techniques to enhance security.
Utilized an autoencoder model for anomaly detection in network traffic.
Applied unsupervised learning to identify potential security breaches without labeled data.
Analyzed performance metrics to evaluate detection accuracy and efficiency.
Achieved improved detection rates compared to traditional methods.
Demonstrated the capability to identify unknown threats effectively.
Reduced false positive rates in intrusion alerts.

Implication

An unsupervised learning model identifies security threats in computing systems, suggesting improved safety measures.

Abstract

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Cite This Study

Patel et al. (2026) studied this question.

synapsesocial.com/papers/69d0af9a659487ece0fa59c6 https://doi.org/10.1504/ijguc.2026.10077384

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