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.

The number of Internet of Things (IoT) devices has been growing rapidly, driven by the demand for low-cost and low-power devices across a wide range of industrial and consumer applications. This expansion has broadened the attack surface exposed to cyber threats, making the detection of malicious activity in the IoT infrastructure a pressing research challenge. Most existing approaches are limited to binary anomaly detection, rely on a single data modality, or incur a computational overhead incompatible with realistic deployment on the edge. This paper introduces the Multi-modal Universal Informer Convolutional Neural Network (MUI-CNN) Classifier, a distributed architecture for multi-class attack detection across heterogeneous IoT sensor and network environments. It combines four components - a multi-modal universal data embedding layer that unifies numerical, categorical, and temporal features into a shared spatio-temporal representation, a CNN local pattern recognition layer, an encoder-only classifier for long-term temporal dependencies, and an optional statistical masking layer for greater sensitivity to low-amplitude signatures. The model is evaluated on the Telecommunication Operating Systems (OS) and Network Internet of Things (TON-IoT) dataset covering eight sensor types and ten attack classes. Results demonstrate F1 above 88% on all IoT sensors and up to 94.85% on the network sensor, confirming that each architectural component contributes a measurable and distinct improvement over partial baselines.