Timeseries XAI in Cybersecurity and Industry

Title	Timeseries XAI in Cybersecurity and Industry
Summary	Timeseries data analysis with XAI in Cybersecurity and Industry
Keywords	Cybersecurity, XAI, timeseries, industryProperty "Keywords" has a restricted application area and cannot be used as annotation property by a user.
TimeFrame	Spring 2026
References	https://www.sciencedirect.com/science/article/pii/S1566253523001148 https://www.nature.com/articles/s41597-025-04603-x
Prerequisites
Author
Supervisor	Grzegorz J. Nalepa, Prayag Tawari, Aurora Esteban
Level	Master
Status	Open

Time series data is ubiquitous — from industrial monitoring systems and energy networks to cybersecurity systems and user activity traces. Understanding temporal patterns is crucial for detecting anomalies, anticipating failures, and supporting human decision-making. Yet, the increasing complexity of time series models makes them difficult to interpret and trust. Industrial and Cybersecurity systems have clearly became a very important area of AI applications recently. From the engineering perspective they produce a large amount of data that can only be analyzed by AI methods.

Explainable Artificial Intelligence (XAI) aims to make models more transparent by uncovering the why behind their predictions. While explainability methods are well-studied for tabular and image data, time series explanations remain a significant open challenge. Temporal dependencies, non-stationarity, and concept drift make it difficult to represent and communicate model reasoning to domain experts.

This project will explore explainable learning and reasoning for time series data, with several possible research directions depending on the student’s interests and available datasets: - Characterising domain-specific dynamics: analysing how time series from different domains (e.g., industrial processes vs. cybersecurity traffic) differ in variability, drifts, or anomaly structure. - Representation learning for interpretability: studying prototypes, motifs, or symbolic rules that capture meaningful temporal patterns. - Counterfactual explanations: developing or adapting methods (e.g., genetic algorithms, motif transformations, gradient perturbations) to generate realistic “what-if” scenarios for time series. - Explainable anomaly detection: integrating interpretability into models that identify abnormal or critical events over time. - Concept drift and model evolution: explaining how and why model behavior changes as time series distributions shift.

The work will be done in connection with the KEEPER project using data from our industrial partners such as Volvo, HMS, Toyota, etc. [[1]]. The project may use as well public data such as Numenta Anomaly Benchmark or UCR/UEA archive.

It is encouraged that this thesis will result in scientific publications possibly also developed in collaboration with external stakeholders.