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アイテム
Practicality of in-kernel/user-space packet processing empowered by lightweight neural network and decision tree
http://hdl.handle.net/10061/0002000669
http://hdl.handle.net/10061/0002000669547f8c09-ced2-4518-9b4a-472425890343
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-11-14 | |||||||
| タイトル | ||||||||
| タイトル | Practicality of in-kernel/user-space packet processing empowered by lightweight neural network and decision tree | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | extended Berkeley Packet Filter (eBPF) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | eXpress Data Path (XDP) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | AF_XDP | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Intrusion detection system (IDS) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Machine learning (ML) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Quantization | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Quantized neural network (NN) | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | Decision tree (DT) | |||||||
| 資源タイプ | ||||||||
| 資源タイプ | journal article | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| 著者 |
原, 崇徳
× 原, 崇徳× Sasabe, Masahiro
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Integrating machine learning (ML) into kernel packet processing, such as extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP), represents a promising strategy for achieving fast and intelligent networking on generic hardware. This includes tasks like automating network operations and discerning traffic classification, exemplified by intrusion detection systems (IDS) combining Decision Tree (DT) and eBPF. However, the potential of ML-empowered packet processing remains to be fully explored. To ensure the integrity and security of kernel operations, eBPF/XDP programs must adhere to stringent constraints such as the maximum number of jump instructions, maximum stack space, and exclusion of floating-point arithmetic. These constraints pose challenges for implementing more intricate ML techniques (e.g., neural networks (NNs)) within eBPF/XDP programs. In such scenarios, AF_XDP provides an alternative solution by allowing XDP programs to redirect packets to user-space applications, bypassing the network stack. This paper initiates an exploration into fast packet classification through two distinct approaches: (1) an in-kernel approach employing eBPF/XDP and (2) a user-space approach assisted by AF_XDP. Specifically, to tackle the eBPF constraints, the in-kernel NN classifier adopts (1) quantization of trained model in the user space, (2) executing the integer-arithmetic-only NN within the kernel space, and (3) sequential layer operations through tail calls. These approaches are evaluated based on factors including packet processing speed, resource efficiency, and detection performance. Notably, our experimental findings demonstrate that (1) Classifiers relying solely on integer arithmetic, such as NN and DT, significantly reduce inference time while maintaining binary classification performance; (2) The lightweight NN classifier can improve the detection performance for most of attacks in case of the multi-class classification compared to the lightweight DT classifier; (3) In single-core scenarios, the DT-empowered in-kernel method can almost achieve the maximum packets per second (pps), i.e., about 800,000 pps, whereas the NN-empowered one exhibits lower pps (i.e., about 450,000 pps); (4) In multi-core scenarios, the NN-empowered packet processing can almost achieve the maximum pps with two or more cores in the AF_XDP approach and four or more cores in the in-kernel approaches. | |||||||
| 書誌情報 |
en : Computer Networks 巻 240, 発行日 2024-01-09 |
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| 出版者 | ||||||||
| 出版者 | Elsevier | |||||||
| ISSN | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 1872-7069 | |||||||
| 出版者版DOI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1016/j.comnet.2024.110188 | |||||||
| 出版者版URI | ||||||||
| 関連タイプ | isReplacedBy | |||||||
| 識別子タイプ | URI | |||||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S1389128624000203 | |||||||
| 権利 | ||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by/4.0/ | |||||||
| 権利情報 | $00A9 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | NA | |||||||
