WEKO3
アイテム
A Design of Network Attack Detection Using Causal and Non-causal Temporal Convolutional Network
http://hdl.handle.net/10228/0002000801
http://hdl.handle.net/10228/00020008012d6c6810-753e-4d31-acec-4be67d6f2d76
| 名前 / ファイル | ライセンス | アクション |
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10435526.pdf (863 KB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||||||
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| 公開日 | 2024-06-19 | |||||||||||||||||
| 資源タイプ | ||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||
| タイトル | ||||||||||||||||||
| タイトル | A Design of Network Attack Detection Using Causal and Non-causal Temporal Convolutional Network | |||||||||||||||||
| 言語 | en | |||||||||||||||||
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| 言語 | eng | |||||||||||||||||
| 著者 |
He, Pengju
× He, Pengju
× 張, 海波
WEKO
35483
× Feng, Yaokai
× Sakurai, Kouichi
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| 抄録 | ||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||
| 内容記述 | Temporal Convolution Network(TCN) has recently been introduced in the cybersecurity field, where two types of TCNs that consider causal relationships are used: causal TCN and non-causal TCN. Previous researchers have utilized causal and non-causal TCNs separately. Causal TCN can predict real-time outcomes, but it ignores traffic data from the time when the detection is activated. Non-causal TCNs can forecast results more globally, but they are less real-time. Employing either causal TCN or non-causal TCN individually has its drawbacks, and overcoming these shortcomings has become an important topic. In this research, we propose a method that combines causal and non-causal TCN in a contingent form to improve detection accuracy, maintain real-time performance, and prevent long detection time. Additionally, we use two datasets to evaluate the performance of the proposed method: NSL-KDD, a well-known dataset for evaluating network intrusion detection systems, and MQTT-IoT-2020, which simulates the MQTT protocol, a standard protocol for IoT machine-to-machine communication. The proposed method in this research increased the detection time by about 0.1ms compared to non-causal TCN when using NSL-KDD, but the accuracy improved by about 1.5%, and the recall improved by about 4%. For MQTT-IoT-2020, the accuracy improved by about 3%, and the recall improved by about 7% compared to causal TCN, but the accuracy decreased by about 1% compared to non-causal TCN. The required time was shortened by 30ms (around 30%), and the recall was improved by about 7%. |
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| 言語 | en | |||||||||||||||||
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| 内容記述タイプ | Other | |||||||||||||||||
| 内容記述 | 5th International Conference, SciSec 2023, July 11–14, 2023, Melbourne, VIC, Australia | |||||||||||||||||
| 言語 | en | |||||||||||||||||
| 書誌情報 |
en : Lecture Notes in Computer Science 巻 14299, p. 513-523, 発行日 2023-11-21 |
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| 出版社 | ||||||||||||||||||
| 出版者 | Springer | |||||||||||||||||
| DOI | ||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||
| 関連識別子 | https://doi.org/10.1007/978-3-031-45933-7_30 | |||||||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||||||
| 関連識別子 | 978-3-031-45932-0 | |||||||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||||||
| 関連識別子 | 978-3-031-45933-7 | |||||||||||||||||
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| 収録物識別子タイプ | PISSN | |||||||||||||||||
| 収録物識別子 | 0302-9743 | |||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||
| 収録物識別子 | 1611-3349 | |||||||||||||||||
| 著作権関連情報 | ||||||||||||||||||
| 権利情報 | Copyright (c) 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-45933-7_30. | |||||||||||||||||
| 出版タイプ | ||||||||||||||||||
| 出版タイプ | AM | |||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||||
| 査読の有無 | ||||||||||||||||||
| 値 | yes | |||||||||||||||||
| 研究者情報 | ||||||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100001768_ja.html | |||||||||||||||||
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| 値 | 10435526 | |||||||||||||||||
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| 値 | 12347 | |||||||||||||||||
