Payload-Aware Intrusion Detection with CMAE and Large Language Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intrusion Detection Systems (IDS) play a vital role in network security, yet signature-based methods are limited by high false positive rates (FPR) and inability to detect novel threats. Recent AI-based approaches offer improved adaptability, but most rely on flow-level or statistical features, constraining their ability to analyze sophisticated payload-based attacks. To address these challenges, we present a dual-path IDS framework: Xavier-CMAE, a lightweight model using Hex2Int tokenization and Xavier initialization, achieves 99.9718% accuracy and a 0.0182% FPR without pre-training; and LLM-CMAE, which leverages pre-trained LLM tokenizers for enhanced detection, achieves 99.9696% accuracy and a 0.0194% FPR at higher computational cost. Experimental results on the CIC-IDS2017 dataset reveal a distinct trade-off between efficiency and Contextually Adept and Scalable (CAS) power, indicating that a modular approach may enable both real-time scalability and in-depth threat analysis. This work advances AI-powered intrusion detection by (1) introducing a modular, payload-centric dual-path architecture that combines lightweight and CAS detection for adaptive, layered security; (2) demonstrating that Xavier-CMAE achieves real-time scalability and state-of-the-art accuracy without embedding pre-training; and (3) exploring the effectiveness and future potential of integrating pre-trained LLM tokenizers for nuanced, selective threat analysis and robust IDS design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it