@inproceedings{ManGia25-IJCLR-IC,
title = {Benchmarking in Neuro-Symbolic AI},
author = {Manhaeve, Robin and Giannini, Francesco and Ali, Mehdi and Azzolini, Damiano and Bizzarri, Alice and Borghesi, Andrea and Bortolotti, Samuele and De Raedt, Luc and Dhami, Devendra and Diligenti, Michelangelo and Sebastijan Dumancic and Boi Faltings and Elisabetta Gentili and Alfonso Gerevini and Marco Gori and Tias Guns and Martin Homola and Kristian Kersting and Jens Lehmann and Michele Lombardi and Luca Lorello and Emanuele Marconato and Stefano Melacci and Andrea Passerini and Debjit Paul and Fabrizio Riguzzi and Stefano Teso and Neil Yorke-Smith and Marco Lippi},
booktitle = {Proceedings of The 4th International Joint Conference on Learning \& Reasoning},
year = {2025}
}
@inproceedings{BizYouJalRigBas24-AIXIA-IC,
author = {Bizzarri, Alice
and Yu, Chung-En
and Jalaian, Brian
and Riguzzi, Fabrizio
and Bastian, Nathaniel D.},
editor = {Artale, Alessandro
and Cortellessa, Gabriella
and Montali, Marco},
title = {Neuro-Symbolic Integration for Open Set Recognition in Network Intrusion Detection},
booktitle = {AIxIA 2024 -- Advances in Artificial Intelligence},
year = {2025},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {50--63},
abstract = {Open Set Recognition (OSR) addresses the challenge of classifying inputs into known and unknown categories, a crucial task where labeling is often prohibitively expensive or incomplete. This is particularly vital in applications like Network Intrusion Detection Systems (NIDS), where OSR is used to identify novel, previously unknown attacks. We propose a neuro-symbolic integration approach that combines deep learning and symbolic methods, enhancing deep embedding for clustering with custom loss functions and leveraging XGBoost's decision tree algorithms. Our methodology not only robustly addresses the identification of previously unknown attacks in NIDS but also effectively manages scenarios involving covariance shift. We demonstrate the efficacy of our approach through extensive experimentation, achieving an AUROC of 0.99 in both contexts. This paper presents a significant step forward in OSR for network intrusion detection by integrating deep and symbolic learning to handle unforeseen challenges in dynamic environments.},
isbn = {978-3-031-80607-0},
doi = {10.1007/978-3-031-80607-0_5}
}
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