A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection
© 2024. The Author(s)..
Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 01. März, Seite 5111 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yin, Xiaochun [VerfasserIn] |
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| Links: |
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| Themen: |
Arbitration mechanism |
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| Anmerkungen: |
Date Revised 04.03.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1038/s41598-024-55814-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369192001 |
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| 100 | 1 | |a Yin, Xiaochun |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Arbitration mechanism | |
| 650 | 4 | |a Dense connection | |
| 650 | 4 | |a Embedding-based bi-LSTM | |
| 650 | 4 | |a LDDoS attacks | |
| 650 | 4 | |a Multi-scale CNN | |
| 650 | 4 | |a Network security | |
| 700 | 1 | |a Fang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zengguang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Deyong |e verfasserin |4 aut | |
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