Payload-Aware Intrusion Detection with CMAE and Large Language Models

2025-03-23

AI Summary

Traditional signature-based intrusion detection systems (IDS) struggle with zero-day attacks and suffer from high false-positive rates, while existing AI-driven approaches rely on coarse-grained traffic or statistical features and lack fine-grained payload-level modeling capability. To address these limitations, this paper proposes a fine-grained IDS framework operating directly on raw network payloads. We introduce two novel architectures: (1) Xavier-CMAE—a pretraining-free model integrating Hex2Int payload encoding, an enhanced convolutional multi-head attention encoder (CMAE); and (2) LLM-CMAE—a lightweight fusion mechanism incorporating LLM-based tokenization embeddings. Experimental results on standard benchmarks demonstrate that Xavier-CMAE achieves 99.971% accuracy and 0.018% false positive rate (FPR), while LLM-CMAE attains 99.969% accuracy and 0.019% FPR—both significantly outperforming Word2Vec and other baselines. The proposed frameworks jointly achieve high detection accuracy, ultra-low FPR, and real-time inference capability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Intrusion Detection Systems (IDS) are crucial for identifying malicious traffic, yet traditional signature-based methods struggle with zero-day attacks and high false positive rates. AI-driven packet-capture analysis offers a promising alternative. However, existing approaches rely heavily on flow-based or statistical features, limiting their ability to detect fine-grained attack patterns. This study proposes Xavier-CMAE, an enhanced Convolutional Multi-Head Attention Ensemble (CMAE) model that improves detection accuracy while reducing computational overhead. By replacing Word2Vec embeddings with a Hex2Int tokenizer and Xavier initialization, Xavier-CMAE eliminates pre-training, accelerates training, and achieves 99.971% accuracy with a 0.018% false positive rate, outperforming Word2Vec-based methods. Additionally, we introduce LLM-CMAE, which integrates pre-trained Large Language Model (LLM) tokenizers into CMAE. While LLMs enhance feature extraction, their computational cost hinders real-time detection. LLM-CMAE balances efficiency and performance, reaching 99.969% accuracy with a 0.019% false positive rate. This work advances AI-powered IDS by (1) introducing a payload-based detection framework, (2) enhancing efficiency with Xavier-CMAE, and (3) integrating LLM tokenizers for improved real-time detection.

Problem

• Improving intrusion detection accuracy and reducing false positives
• Enhancing efficiency in payload-based attack pattern detection
• Integrating LLM tokenizers for better real-time detection performance

Innovation

• Enhanced CMAE model with Hex2Int tokenizer
• Xavier initialization for faster training
• Integrated LLM tokenizers for feature extraction