AICCE: AI Driven Compliance Checker Engine
Mohammad Wali Ur Rahman, Martin Manuel Lopez, Lamia Tasnim Mim, Carter Farthing, Julius Battle, Kathryn Buckley, Salim Hariri

TL;DR
AICCE is an AI-driven system that automates IPv6 compliance verification using dual-architecture reasoning and retrieval-augmented generation, improving accuracy and robustness over traditional rule-based methods.
Contribution
The paper introduces AICCE, a novel generative system combining explainability and script execution modes for scalable, interpretable, and accurate compliance checking in IPv6 traffic.
Findings
Achieves up to 99% accuracy and F1-score on IPv6 samples.
Effectively detects routine and covert non-compliance in communication protocols.
Outperforms conventional rule-based compliance systems.
Abstract
For digital infrastructure to be safe, compatible, and standards-aligned, automated communication protocol compliance verification is crucial. Nevertheless, current rule-based systems are becoming less and less effective since they are unable to identify subtle or intricate non-compliance, which attackers frequently use to establish covert communication channels in IPv6 traffic. In order to automate IPv6 compliance verification, this paper presents the Artificial Intelligence Driven Compliance Checker Engine (AICCE), a novel generative system that combines dual-architecture reasoning and retrieval-augmented generation (RAG). Specification segments pertinent to each query can be efficiently retrieved thanks to the semantic encoding of protocol standards into a high-dimensional vector space. Based on this framework, AICCE offers two complementary pipelines: (i) Explainability Mode, which…
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