Detecting Faults in Telecom Software Using Diffusion Models : A proof of concept study for the application of diffusion models on Telecom data

Abstract: This thesis focuses on software fault detection in the telecom industry, which is crucial for companies like Ericsson to ensure stable and reliable software. Given the importance of software performance to companies that rely on it, automatically detecting faulty behavior in test or operational environments is challenging. Several approaches have been proposed to address this problem. This thesis explores reconstruction-based and forecasting-based anomaly detection using diffusion models to address software failure detection. To this end, the usage of the Structured State Space Sequence Diffusion Model was explored, which can handle temporal dependencies of varying lengths. The numerical time series data results were promising, demonstrating the model’s effectiveness in capturing and reconstructing the underlying patterns, particularly with continuous features. The contributions of this thesis are threefold: (i) A proposal of a framework for utilizing diffusion models for Time Series anomaly detection, (ii) a proposal of a particular Diffusion model Architecture that is capable of outperforming existing Ericsson Solutions on an anomaly detection dataset, (iii) presentation of experiments and results which add extra insight into the model’s capabilities, exposing some of its limitations and suggesting future research avenues to enhance its capabilities further.