AICCE: AI Driven Compliance Checker Engine

• URL: http://arxiv.org/abs/2604.03330v1
• Date: Fri, 03 Apr 2026 00:45:24 GMT
• Title: AICCE: AI Driven Compliance Checker Engine
• Abstract summary: Artificial Intelligence Driven Compliance Checker Engine (AICCE) is a novel generative system that combines dual-architecture reasoning and retrieval-augmented generation (RAG)<n>With the debate mechanism enhancing decision reliability, AICCE achieves accuracy and F1-scores of up to 99% when tested on IPv6 packet samples across sixteen cutting-edge generative models.
• Score: 0.26385121748044166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 uses parallel LLM agents to render decisions and settle disputes through organized discussions to improve interpretability and robustness, and (ii) Script Execution Mode, which converts clauses into Python rules that can be executed quickly for dataset-wide verification. With the debate mechanism enhancing decision reliability in complicated scenarios and the script-based pipeline lowering per-sample latency, AICCE achieves accuracy and F1-scores of up to 99% when tested on IPv6 packet samples across sixteen cutting-edge generative models. By offering a scalable, auditable, and generalizable mechanism for identifying both routine and covert non-compliance in dynamic communication environments, our results show that AICCE overcomes the blind spots of conventional rule-based compliance checking systems.

Related papers

• FDIFormer:Protocol-Aware Transformer Learning for False Data Injection Attack Detection in Smart Grid Networks [1.5761916307614143]
  FDIFormer is a feature-free framework for FDI attack detection using structured textual representations of GOOSE packet sequences and fine-tuned pre-trained Transformer models.<n>The framework converts protocol packets into structured text windows that capture communication behaviour, enabling Transformer models to learn attack-related patterns directly from the data.
  arXiv  Detail & Related papers  (2026-07-07T12:37:21Z)
• Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems [1.784933900656067]
  We study whether an off-the-shelf large language model (LLM) can serve as a complementary, human-in-the-loop layer for ICS detectors.<n>We cast this as a binary network-side normal/critical decision task on two public ICS datasets.<n>LLM produces a normal/critical alert together with a concise, token-grounded incident record for analyst review.
  arXiv  Detail & Related papers  (2026-04-28T19:44:38Z)
• WISV: Wireless-Informed Semantic Verification for Distributed Speculative Decoding in Device-Edge LLM Inference [56.297697169678095]
  WISV (Wireless-Informed Semantic Verification) is a novel distributed speculative decoding framework.<n>We show that WISV achieves up to a 60.8% increase in accepted length, a 37.3% reduction in interaction rounds, and a 31.4% improvement in end-to-end latency.<n>We validate WISV on a hardware testbed comprising an NVIDIA Jetson AGX Orin and an A40-equipped server.
  arXiv  Detail & Related papers  (2026-04-20T01:29:56Z)
• SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks [17.028162341456373]
  6G networks promise to transform the telecom industry by enabling dynamic resource allocation, predictive maintenance, and ultra-reliable low-latency communications.<n>However, the deployment of 6G systems results in severe data scarcity, hindering the training of efficient AI models.<n>We propose the Synthetic Data Generation with Ethics Audit Loop (SEAL) framework, which extends baseline pipelines with an Ethical and Regulatory Compliance by Design (ERCD) module and a Federated Learning (FL) feedback system.
  arXiv  Detail & Related papers  (2026-04-02T15:03:24Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• Intent2QoS: Language Model-Driven Automation of Traffic Shaping Configurations [6.027356970659455]
  Traffic shaping and Quality of Service (QoS) enforcement are critical for managing bandwidth, latency, and fairness in networks.<n>This paper presents an automated framework that converts high-level traffic shaping intents in natural or declarative language into valid and correct traffic control rules.
  arXiv  Detail & Related papers  (2026-01-26T21:18:14Z)
• An Agentic Framework with LLMs for Solving Complex Vehicle Routing Problems [66.60904891478687]
  We propose an Agentic Framework with LLMs (AFL) for solving complex vehicle routing problems.<n>AFL directly extracts knowledge from raw inputs and enables self-contained code generation.<n>We show that AFL substantially outperforms existing LLM-based baselines in both code reliability and solution feasibility.
  arXiv  Detail & Related papers  (2025-10-19T03:59:25Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Good Enough to Learn: LLM-based Anomaly Detection in ECU Logs without Reliable Labels [0.0]
  Anomaly detection often relies on supervised or clustering approaches, with limited success in specialized domains like automotive communication systems.<n>We propose a novel decoder-only Large Language Model (LLM) to detect anomalies in Electronic Control Unit (ECU) communication logs.
  arXiv  Detail & Related papers  (2025-07-01T14:56:09Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols [28.04609776570199]
  Large AI Models (LAMs) are key enablers of the AI-Native Air Interface (AI-AI)<n>This paper presents the first standards-compliant emulation of the Radio Resource Control layer using a decoder-only LAM.<n>Results demonstrate that LAMs, when augmented with protocol-aware reasoning, can directly orchestrate control-plane procedures.
  arXiv  Detail & Related papers  (2025-05-22T15:55:56Z)
• CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection [41.94295877935867]
  We introduce a benchmark to assess the ability of Large Language Models to autonomously identify vulnerabilities in new cryptographic protocols. We created a dataset of novel, flawed, communication protocols and designed a method to automatically verify the vulnerabilities found by the AI agents.
  arXiv  Detail & Related papers  (2024-11-20T14:16:55Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.