Literature Database Entry

basaran2025xainomaly

Osman Tugay Basaran and Falko Dressler, "XAInomaly: Explainable, Interpretable and Trustworthy AI for Next Generation Ultra-reliable Low-latency Communications (xURLLC) in 6G Networks," Proceedings of 39th AAAI Conference on Artificial Intelligence (AAAI 2025), AAAI-25 Bridge on Explainable AI, Energy and Critical Infrastructure Systems, Poster Session, Philadelphia, PA, February 2025. (to appear)

Abstract

6G networks are designed to support mission-critical applications, such as remote healthcare services, autonomous vehicles, AI-guided industrial automation, and the tactile internet, all of which rely on xURLLC. Ensuring network reliability in these contexts requires robust anomaly detection mechanisms that can proactively identify and address potential disruptions. However, traditional AI/ML-based anomaly detection systems often lack interpretability, creating barriers to trust and adoption. To address this, we propose a novel reactive Explainable AI (XAI) framework tailored for real-time anomaly detection in 6G networks.

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Osman Tugay Basaran
Falko Dressler

BibTeX reference

@inproceedings{basaran2025xainomaly,
    author = {Basaran, Osman Tugay and Dressler, Falko},
    note = {to appear},
    title = {{XAInomaly: Explainable, Interpretable and Trustworthy AI for Next Generation Ultra-reliable Low-latency Communications (xURLLC) in 6G Networks}},
    publisher = {AAAI},
    address = {Philadelphia, PA},
    booktitle = {39th AAAI Conference on Artificial Intelligence (AAAI 2025), AAAI-25 Bridge on Explainable AI, Energy and Critical Infrastructure Systems, Poster Session},
    month = {2},
    year = {2025},
   }
   
   

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