Timeseries XAI in Cybersecurity and Industry - Revision history

Aurora at 13:30, 24 October 2025

2025-10-24T13:30:08Z

Islab: Islab moved page Cybersec XAI 4 TS to Timeseries XAI in Cybersecurity and Industry

2025-10-24T09:46:06Z

Islab moved page Cybersec XAI 4 TS to Timeseries XAI in Cybersecurity and Industry

Islab at 09:43, 24 October 2025

2025-10-24T09:43:10Z

Islab: Created page with "{{StudentProjectTemplate |Summary=Cybersecurity data analysis with XAI |Keywords=Cybersecurity, XAI, timeseries |TimeFrame=Spring 2026 |References=https://www.sciencedirect.co..."

2025-10-22T22:45:15Z

Created page with "{{StudentProjectTemplate |Summary=Cybersecurity data analysis with XAI |Keywords=Cybersecurity, XAI, timeseries |TimeFrame=Spring 2026 |References=https://www.sciencedirect.co..."

New page

{{StudentProjectTemplate
|Summary=Cybersecurity data analysis with XAI
|Keywords=Cybersecurity, XAI, timeseries
|TimeFrame=Spring 2026
|References=https://www.sciencedirect.com/science/article/pii/S1566253523001148
https://www.nature.com/articles/s41597-025-04603-x
|Supervisor=Grzegorz J. Nalepa, Prayag Tawari, Aurora Esteban
|Level=Master
|Status=Open
}}
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.
Thus in this thesis we would like to consider AI-based timeseries analysis with applications and data from cybersecurity systems.
Some of the general questions are
1) what makes this specific domain distinct wrt to data and its characteristics? e.g. rapid changes, anomalies, drifts, data imbalance?
2) what types of explanations could be most useful for cybersec experts, e.g. counterfactuals, prototypes

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

@@ Line 5: / Line 5: @@
 |References=https://www.sciencedirect.com/science/article/pii/S1566253523001148
 https://www.nature.com/articles/s41597-025-04603-x
-|Supervisor=Grzegorz J. Nalepa, Prayag Tawari, Aurora Esteban
+|Supervisor=Grzegorz J. Nalepa, Prayag Tawari, Aurora Esteban Toscano
 |Level=Master
 |Status=Open

← Older revision	Revision as of 09:46, 24 October 2025
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@@ Line 1: / Line 1: @@
 {{StudentProjectTemplate
-|Summary=Cybersecurity data analysis with XAI
+|Summary=Timeseries data analysis with XAI in Cybersecurity and Industry
-|Keywords=Cybersecurity, XAI, timeseries
+|Keywords=Cybersecurity, XAI, timeseries, industry
 |TimeFrame=Spring 2026
 |References=https://www.sciencedirect.com/science/article/pii/S1566253523001148
@@ Line 9: / Line 9: @@
 |Status=Open
 }}
-Cybersecurity systems have clearly became a very important area of AI applications recently.
+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.
-From the engineering perspective they produce a large amount of data that can only be analyzed by AI methods.
+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.
-Thus in this thesis we would like to consider AI-based timeseries analysis with applications and data from cybersecurity systems.
-Some of the general questions are
+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.
-) what makes this specific domain distinct wrt to data and its characteristics? e.g. rapid changes, anomalies, drifts, data imbalance?
-) what types of explanations could be most useful for cybersec experts, e.g. counterfactuals, prototypes
+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:
 It is encouraged that this thesis will result in scientific publications possibly also developed in collaboration with external stakeholders.