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DeepWeave is presented, a probabilistic neuro-symbolic Network Intrusion Detection System (NIDS) that makes logic an integral part of detection, and demonstrates label-efficient, adaptable, and inspectable neuro-symbolic intrusion detection.
A human-centered investigation into LLM-based NIDS rule engineering is presented, formalizing a grounded generation framework and evaluating it through a user study with 10 domain experts, revealing a syntax-semantics paradox.
This paper claims that an in-depth investigation of the adversarial robustness of NeSy models is necessary and provides the first systematic evaluation of backdoor attacks against NeSy, and shows that while NeSy models are indeed more robust than their neural counterpart on average, their robustness vastly depend on th...
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