Autors: Gotseva N., Vlahov, A. G., Mfondoum, R. B., Ivanov, A. S., Poulkov, V. K.
Title: A Comparative Analysis of Anomaly Detection Techniques in Cellular Data
Keywords: Anomaly detection, Cellular network analytics, LSTM-Autoencoder, One-Class SVM, Outlier detection, Transformer-based anomaly detection

Abstract: In this paper we compare various anomaly detection techniques for cellular network data using a multivariate dataset from Milan and Trentino. We evaluate traditional statistical methods (Z-score, IQR), machine learning (One-Class SVM), and deep learning approaches (LSTM-Autoencoder, Transformer). Experimental results indicate that deep learning models significantly outperform traditional methods in terms of both accuracy and efficiency. The Transformer model achieved 96.5% accuracy in 23 epochs, compared to 93% by the LSTM-Autoencoder in 40 epochs and 86% by the One-Class SVM.

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Issue

60th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2025 - Proceedings, 2025, Macedonia, https://doi.org/10.1109/ICEST66328.2025.11098230

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Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus