| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-03-12 |
| タイトル |
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タイトル |
Unsupervised Real-Time In-Kernel Intrusion Detection System Using Autoencoders and eBPF |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
intrusion detection system (IDS) |
| キーワード |
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主題Scheme |
Other |
|
主題 |
extended Berkeley packet filter (eBPF) |
| キーワード |
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主題Scheme |
Other |
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主題 |
quantization-aware training (QAT) |
| キーワード |
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主題Scheme |
Other |
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主題 |
unsupervised learning |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Taguchi, Hotaka
原, 崇徳
笠原, 正治
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Traditional intrusion detection systems (IDSs), leveraging machine learning (ML) algorithms, have improved the detection accuracy of unknown attacks by continuously updating ML models but have underestimated the context switching overhead between kernel and user spaces. To address this issue, existing studies have implemented real-time IDSs using neural networks (NNs) in the kernel space by offloading the quantized models trained with post-training quantization (PTQ) to extended Berkeley Packet Filter (eBPF). However, they cannot fine-tune the model parameters through the additional training because the PTQ applies the quantization to the trained model. In addition, their IDSs are based on supervised learning, which requires a large amount of labeled data. In this paper, we propose a real-time in-kernel IDS leveraging eBPF, unsupervised learning, and quantization-aware training (QAT) to enhance continuous learning. Evaluation results demonstrate that the proposed in-kernel IDS exhibits almost the same detection accuracy as the traditional user-space IDS. From the viewpoint of the packet processing speed, the proposed in-kernel IDS can serve 224 K packets per second while the user-space IDS can only serve 3.2 K packets per second. |
| 書誌情報 |
en : IEICE Transactions on Communications
巻 E109-B,
号 2,
p. 326-336,
ページ数 11,
発行日 2025-08-19
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| 出版者 |
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出版者 |
IEICE |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1745-1345 |
| 出版者版DOI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.23919/transcom.2025EBP3028 |
| 出版者版URI |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/abstract/document/11130683 |
| 権利 |
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権利情報 |
Copyright © 2026 The Institute of Electronics, Information and Communication Engineers |
| 著者版フラグ |
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出版タイプ |
VoR |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
24K02931 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-24K02931/ |
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研究課題名 |
次世代分散型インターネットに向けた耐結託性をもつ合意形成メカニズム |
| 助成情報 |
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助成機関名 |
Japan Society for the Promotion of Science (JSPS) |
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研究課題番号 |
23K16869 |
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研究課題番号URI |
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K16869/ |
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研究課題名 |
コンテナ技術に基づく自己変革能力を備えたネットワークスライスモビリティの実現 |
fulltext (4.2 MB)
