  In today's hyper-connected world, ensuring the reliability of telecom
networks becomes increasingly crucial. Telecom networks encompass numerous
underlying and intertwined software and hardware components, each providing
different functionalities. To ensure the stability of telecom networks, telecom
software, and hardware vendors developed several methods to detect any aberrant
behavior in telecom networks and enable instant feedback and alerts. These
approaches, although powerful, struggle to generalize due to the unsteady
nature of the software-intensive embedded system and the complexity and
diversity of multi-standard mobile networks. In this paper, we present a system
to detect anomalies in telecom networks using a generative AI model. We
evaluate several strategies using diffusion models to train the model for
anomaly detection using multivariate time-series data. The contributions of
this paper are threefold: (i) A proposal of a framework for utilizing diffusion
models for time-series anomaly detection in telecom networks, (ii) A proposal
of a particular Diffusion model architecture that outperforms other
state-of-the-art techniques, (iii) Experiments on a real-world dataset to
demonstrate that our model effectively provides explainable results, exposing
some of its limitations and suggesting future research avenues to enhance its
capabilities further.
