| Autor: |
Jin Wang, Liping Wang, Ruiqing Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Mathematical Biosciences and Engineering, Vol 21, Iss 3, Pp 4187-4209 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1551-0018 |
| DOI: |
10.3934/mbe.2024185?viewType=HTML |
| Popis: |
Low rate distributed denial of service attack (LR-DDoS) is a special type of distributed denial of service (DDoS) attack, which uses the vulnerability of HTTP protocol to send HTTP requests to applications or servers at a slow speed, resulting in long-term occupation of server threads and affecting the normal access of legitimate users. Since LR-DDoS attacks do not need to send flooding or a large number of HTTP requests, it is difficult for traditional intrusion detection methods to detect such attacks, especially when HTTP traffic is encrypted. To overcome the above problems, we proposed an encrypted LR-DDoS attack detection and mitigation method based on the multi-granularity feature fusion (MFFLR-DDoS) for software defined networking (SDN). This method analyzes the encrypted session flow from the time sequence of packets and the spatiality of session flow and uses different deep learning methods to extract features, to obtain more effective features for abnormal traffic detection. In addition, we used the advantages of SDN architecture to perform real-time defense against LR-DDoS attacks by the way of SDN controller issuing flow rules. The experimental results showed that the MFFLR-DDoS model had a higher detection rate than advanced methods, and could mitigate LR-DDoS attack traffic online and in real-time. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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