Abstract
A neuro-symbolic system detects compromise of business accounts using an agentic investigation stage and a knowledge-based decision stage. An LLM agent invokes multiple investigative tools via a model context protocol and produces an initial assessment with a rationale. The rationale and tool results are normalized into a fixed-dimensional signal vector. A hybrid FOIL and inverse entailment inductive logic programming procedure discovers interpretable confirmation and rejection rules from examples and background predicates. Rule firings and signal features are converted into multi-order features and scored using a Naïve Bayes classifier with smoothed conditional probability tables. A contradiction solver identifies disagreement patterns among the agent, symbolic rules, and probabilistic scores and applies veto and rescue logic. An ensemble module outputs a final classification and an explanation package including fired rules and contradiction indicators.
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Recommended Citation
Anonymous, "Neuro-Symbolic Business Account Compromise Detection with Agentic AI and Inductive Knowledge Discovery", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10616