Payload-Aware Intrusion Detection with CMAE and Large Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916663377526784 |
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| author | Kim, Yongcheol Lee, Chanjae Yoon, Young |
| author_facet | Kim, Yongcheol Lee, Chanjae Yoon, Young |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Payload-Aware Intrusion Detection with CMAE and Large Language Models Kim, Yongcheol Lee, Chanjae Yoon, Young Cryptography and Security Artificial Intelligence 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. |
| title | Payload-Aware Intrusion Detection with CMAE and Large Language Models |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2503.20798 |