Mask Inflation Encoder and Quasi-Dynamic Thresholding Outlier Detection in Cellular Networks

Mobile networks have advanced significantly, providing high-throughput voice, video, and integrated data access to support connectivity through various services to facilitate high user density. This traffic growth has also increased the complexity of outlier detection (OD) for fraudster identification, fault detection, and protecting network infrastructure and its users against cybersecurity threats. Autoencoder (AE) models are widely used for outlier detection (OD) on unlabeled and temporal data; however, they rely on fixed anomaly thresholds and anomaly-free training data, which are both difficult to obtain in practice. This paper introduces statistical masking in the encoder to enhance learning from nearly normal data by flagging potential outliers. It also proposes a quasidynamic threshold mechanism that adapts to reconstruction errors, improving detection by up to 3% median area under the receiver operating characteristic (AUROC) compared to the standard 95% threshold used in base AE models. Extensive experiments on the Milan Human Telecommunications Interaction (HTA) dataset validate the performance of the proposed methods. Combined, these two techniques yield a 31% improvement in AUROC and a 34% lower computational complexity when compared to baseline AE, long short-term memory AE (LSTM-AE), and seasonal auto-regressive integrated moving average (SARIMA), enabling efficient OD in modern cellular networks.