Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks

TL;DR

This paper presents a novel Multi-Scale Convolutional LSTM model with Transfer Learning for efficient anomaly detection in cellular networks, reducing training time and improving accuracy across different datasets.

Contribution

Introduces a new Multi-Scale Convolutional LSTM model combined with Transfer Learning for cellular network anomaly detection, addressing data scarcity and retraining challenges.

Findings

Model trained from scratch achieves 99% accuracy after 100 epochs.

Fine-tuned model reaches 95% accuracy after 20 epochs.

Transfer Learning reduces training time and improves adaptability.

Abstract

The rapid growth in mobile broadband usage and increasing subscribers have made it crucial to ensure reliable network performance. As mobile networks grow more complex, especially during peak hours, manual collection of Key Performance Indicators (KPIs) is time-consuming due to the vast data involved. Detecting network failures and identifying unusual behavior during busy periods is vital to assess network health. Researchers have applied Deep Learning (DL) and Machine Learning (ML) techniques to understand network behavior by predicting throughput, analyzing call records, and detecting outages. However, these methods often require significant computational power, large labeled datasets, and are typically specialized, making retraining for new scenarios costly and time-intensive. This study introduces a novel approach Multi-Scale Convolutional LSTM with Transfer Learning (TL) to detect anomalies in cellular networks. The model is initially trained from scratch using a publicly available dataset to learn typical network behavior. Transfer Learning is then employed to fine-tune the model by applying learned weights to different datasets. We compare the performance of the model trained from scratch with that of the fine-tuned model using TL. To address class imbalance and gain deeper insights, Exploratory Data Analysis (EDA) and the Synthetic Minority Over-sampling Technique (SMOTE) are applied. Results demonstrate that the model trained from scratch achieves 99% accuracy after 100 epochs, while the fine-tuned model reaches 95% accuracy on a different dataset after just 20 epochs.