Introducing a Multi-Perspective xAI Tool for Better Model Explainability-Reference-Cited by-同舟云学术

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Introducing a Multi-Perspective xAI Tool for Better Model Explainability

Published:2024-07-30 Issue: Volume: Page:1-8
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
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Author:

Pawlicki Marek¹^ORCID,Puchalski Damian²^ORCID,Szelest Sebastian²^ORCID,Pawlicka Aleksandra³^ORCID,Kozik Rafał¹^ORCID,Choraś Michał¹^ORCID

Affiliation:

1. Bydgoszcz University of Science and Technology, Poland and ITTI Sp. z o.o., Poland

2. ITTI Sp. z o.o., Poland

3. ITTI Sp. z o.o., Poland and University of Warsaw, Poland

Funder

Horizon 2020 Framework Programme

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3670905

Reference32 articles.

1. 2024. AI4CYBER web page at the European Commission’s Cordis portal. https://cordis.europa.eu/project/id/101070450. Accessed April 23, 2024.

2. 2024. Trustworthy Artificial Intelligence for Cybersecurity Reinforcement and System Resilience (AI4CYBER). https://ai4cyber.eu/. Accessed April 23, 2024.

3. “Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks

4. Visualizing the effects of predictor variables in black box supervised learning models

5. Osvaldo Arreche, Tanish R Guntur, Jack W Roberts, and Mustafa Abdallah. 2024. E-XAI: Evaluating Black-Box Explainable AI Frameworks for Network Intrusion Detection. IEEE Access (2024).

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