Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoring
arXiv:2606.06871v1 Announce Type: new Abstract: Diagnosing 802.11 packet captures requires expert protocol knowledge, is slow, inconsistent across engineers, and unscalable. LLM-based approaches sound plausible but fabricate protocol events absent from captures (especially truncated traces), produce uncalibrated confidence scores, and suffer evaluation bias when golden references are co-produced by the model under test. We introduce PROBE (Protocol Reasoning Over evidence-Based Ensembles), a multi-stage pipeline addressing all three failures. It integrates (i) deterministic PCAP-to-text normal
The proliferation of complex wireless networks and increasing reliance on AI for diagnostic tasks necessitate more reliable and interpretable automation.
This development addresses critical limitations in applying large language models to technical diagnostics, particularly in areas requiring high accuracy and evidence-based reasoning, thus impacting the robustness of AI-driven automation.
The proposed PROBE pipeline offers a more reliable method for 802.11 packet capture diagnosis, moving beyond the current limitations of LLMs that generate uncalibrated confidence scores and fabricate events.
- · Network security professionals
- · IT departments
- · Wireless network equipment vendors
- · AI diagnostic tool developers
- · LLMs without robust evidence-grounding mechanisms
- · Manual packet capture analysts
- · Companies relying on uncalibrated AI diagnostics
Improved network diagnostic efficiency and accuracy through automated, reliable analysis of complex wireless data.
Increased trust and adoption of AI systems in critical infrastructure and technical fault diagnosis, expanding their utility beyond creative or general tasks.
Development of regulatory standards or best practices for 'evidence-grounded' AI in technical fields due to demonstrable reliability and reduced hallucination risk.
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Read at arXiv cs.LG