# Anomaly Detection for IoT Global Connectivity

arXiv: https://arxiv.org/abs/2508.09660 · HTML: https://mlsanity.com/paper/2508.09660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art](https://mlsanity.com/paper/2004.00433/) (2020)
- [Massive Exploration of Neural Machine Translation Architectures](https://mlsanity.com/paper/1703.03906/) (2017)
- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Time Series Anomaly Detection for Smart Grids: A Survey](https://mlsanity.com/paper/2107.08835/) (2021)
- [Human readable network troubleshooting based on anomaly detection and feature scoring](https://mlsanity.com/paper/2108.11807/) (2021)
- [Where Things Roam: Uncovering Cellular IoT/M2M Connectivity](https://mlsanity.com/paper/2007.13708/) (2020)
- [A first look at the IP eXchange Ecosystem](https://mlsanity.com/paper/2007.13809/) (2020)

## Abstract (arXiv)

Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it has become increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a reactive approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR -- an unsupervised anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic. We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.