The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN
Named Data Networking (NDN) is one of the main research projects of the information center network (ICN), and its efficient forwarding mechanism attaches the attention of researchers. Like other networks, NDN also faces the threat of cyber attacks. With the assistance of the colluding server, the Co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9139197/ |
| _version_ | 1818351366200885248 |
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| author | Liang Liu Wenzhi Feng Zhijun Wu Meng Yue Rudan Zhang |
| author_facet | Liang Liu Wenzhi Feng Zhijun Wu Meng Yue Rudan Zhang |
| author_sort | Liang Liu |
| collection | DOAJ |
| description | Named Data Networking (NDN) is one of the main research projects of the information center network (ICN), and its efficient forwarding mechanism attaches the attention of researchers. Like other networks, NDN also faces the threat of cyber attacks. With the assistance of the colluding server, the Collusive Interest Flooding Attacks (CIFA) can use the defects of the NDN's internal forwarding mechanism to send malicious interest packets in the form of pulses. It affects the normal requests of legitimate users and reduces the quality of NDN network services in this way. By analyzing the characteristics of network traffic and CIFA model, a new CIFA detecting method based on the prediction error between particle filter and one-step prediction algorithm is proposed. This scheme samples the network traffic and judges whether the network is under attack by comparing the normalized error value of the one-step prediction and the estimate of the particle filter. Experimental analysis shows that the detection scheme in this paper has higher detection rate than the existing detection schemes. |
| first_indexed | 2024-12-13T18:36:36Z |
| format | Article |
| id | doaj.art-598e9f36c8874a6b9110214189c90132 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T18:36:36Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-598e9f36c8874a6b9110214189c901322022-12-21T23:35:21ZengIEEEIEEE Access2169-35362020-01-01812800512801710.1109/ACCESS.2020.30087239139197The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDNLiang Liu0https://orcid.org/0000-0002-8822-8354Wenzhi Feng1https://orcid.org/0000-0002-3264-7138Zhijun Wu2https://orcid.org/0000-0002-0691-1767Meng Yue3https://orcid.org/0000-0002-1473-3729Rudan Zhang4School of Electronics and Information and Automation, Civil Aviation University of China, Tianjin, ChinaSchool of Electronics and Information and Automation, Civil Aviation University of China, Tianjin, ChinaSchool of Electronics and Information and Automation, Civil Aviation University of China, Tianjin, ChinaSchool of Electronics and Information and Automation, Civil Aviation University of China, Tianjin, ChinaSchool of Electronics and Information and Automation, Civil Aviation University of China, Tianjin, ChinaNamed Data Networking (NDN) is one of the main research projects of the information center network (ICN), and its efficient forwarding mechanism attaches the attention of researchers. Like other networks, NDN also faces the threat of cyber attacks. With the assistance of the colluding server, the Collusive Interest Flooding Attacks (CIFA) can use the defects of the NDN's internal forwarding mechanism to send malicious interest packets in the form of pulses. It affects the normal requests of legitimate users and reduces the quality of NDN network services in this way. By analyzing the characteristics of network traffic and CIFA model, a new CIFA detecting method based on the prediction error between particle filter and one-step prediction algorithm is proposed. This scheme samples the network traffic and judges whether the network is under attack by comparing the normalized error value of the one-step prediction and the estimate of the particle filter. Experimental analysis shows that the detection scheme in this paper has higher detection rate than the existing detection schemes.https://ieeexplore.ieee.org/document/9139197/Named data networkingcollusive interest flood attacksprediction errorone-step predictionparticle filter |
| spellingShingle | Liang Liu Wenzhi Feng Zhijun Wu Meng Yue Rudan Zhang The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN IEEE Access Named data networking collusive interest flood attacks prediction error one-step prediction particle filter |
| title | The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN |
| title_full | The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN |
| title_fullStr | The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN |
| title_full_unstemmed | The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN |
| title_short | The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN |
| title_sort | detection method of collusive interest flooding attacks based on prediction error in ndn |
| topic | Named data networking collusive interest flood attacks prediction error one-step prediction particle filter |
| url | https://ieeexplore.ieee.org/document/9139197/ |
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