Research Article
Improving Anomaly Detection Accuracy Using Fuzzy Cuckoo-Inspired Clustering and Optimization Techniques
Issue:
Volume 11, Issue 2, December 2026
Pages:
63-75
Received:
16 February 2026
Accepted:
1 June 2026
Published:
22 July 2026
DOI:
10.11648/j.mlr.20261102.11
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Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. To overcome these limitations, this study proposes a novel Fuzzy Cuckoo-Based Clustering Technique (F-CBCT) that integrates fuzzy logic with cuckoo search-based clustering and optimization. The proposed framework employs a decision tree classifier enhanced with fuzzy membership functions, enabling effective management of uncertainty during classification. Model parameters are optimized using a hybrid strategy based on Mean Square Error (MSE) and the Silhouette Index, improving clustering quality and classification accuracy. Experimental evaluations conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, achieving a 96.86% detection rate, 97.77% accuracy, a 1.297% false positive rate, and an F-measure of 98.30%. Comparative analysis with existing state-of-the-art anomaly detection methods confirms that F-CBCT consistently outperforms conventional approaches in terms of detection capability, robustness, and reliability. The proposed technique effectively reduces false alarms while maintaining high detection performance, making it a promising solution for real-world anomaly detection applications across diverse and dynamic environments.
Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. T...
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,
Ram Kumar Nagarch*
