MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack
Abstract A new type of Collusive Interest Flooding Attack (CIFA), Improved Collusive Interest Flooding Attack (I‐CIFA), which originates from CIFA with a stronger concealment, higher attack effect, lower attack cost, and wider attack range in Named Data Networking (NDN). In order to detect this atta...
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | IET Information Security |
| Online Access: | https://doi.org/10.1049/ise2.12100 |
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| _version_ | 1849765225971056640 |
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| author | Meng Yue Silin Peng Wenzhi Feng |
| author_facet | Meng Yue Silin Peng Wenzhi Feng |
| author_sort | Meng Yue |
| collection | DOAJ |
| description | Abstract A new type of Collusive Interest Flooding Attack (CIFA), Improved Collusive Interest Flooding Attack (I‐CIFA), which originates from CIFA with a stronger concealment, higher attack effect, lower attack cost, and wider attack range in Named Data Networking (NDN). In order to detect this attack, the present study explores new detection features and establishes a sample set of attack features with different granularities, and accordingly, the Pearson coefficient is used to validate the correlation between the proposed features and the network states. Finally, the Random Forest model is designed to detect the I‐CIFA attack. To evaluate the performance of the approach, extensive experiments are conducted in ndnSIM platform. Test results show that the proposed detection approach outperforms other existing approaches with a detection rate of 98.1%, error rate of 1.9%, and false positive rate of 1.5%. |
| format | Article |
| id | doaj-art-bc6b2f615dda4f13a317a4cde4278899 |
| institution | DOAJ |
| issn | 1751-8709 1751-8717 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Information Security |
| spelling | doaj-art-bc6b2f615dda4f13a317a4cde42788992025-08-20T03:04:55ZengWileyIET Information Security1751-87091751-87172023-05-0117336037610.1049/ise2.12100MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attackMeng Yue0Silin Peng1Wenzhi Feng2College of Safety Science and Engineering Civil Aviation University of China Tianjin ChinaCollege of Electronic Information and Automation Civil Aviation University of China Tianjin ChinaUniversity of the Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing ChinaAbstract A new type of Collusive Interest Flooding Attack (CIFA), Improved Collusive Interest Flooding Attack (I‐CIFA), which originates from CIFA with a stronger concealment, higher attack effect, lower attack cost, and wider attack range in Named Data Networking (NDN). In order to detect this attack, the present study explores new detection features and establishes a sample set of attack features with different granularities, and accordingly, the Pearson coefficient is used to validate the correlation between the proposed features and the network states. Finally, the Random Forest model is designed to detect the I‐CIFA attack. To evaluate the performance of the approach, extensive experiments are conducted in ndnSIM platform. Test results show that the proposed detection approach outperforms other existing approaches with a detection rate of 98.1%, error rate of 1.9%, and false positive rate of 1.5%.https://doi.org/10.1049/ise2.12100 |
| spellingShingle | Meng Yue Silin Peng Wenzhi Feng MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack IET Information Security |
| title | MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack |
| title_full | MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack |
| title_fullStr | MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack |
| title_full_unstemmed | MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack |
| title_short | MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack |
| title_sort | mf rf a detection approach based on multi features and random forest algorithm for improved collusive interest flooding attack |
| url | https://doi.org/10.1049/ise2.12100 |
| work_keys_str_mv | AT mengyue mfrfadetectionapproachbasedonmultifeaturesandrandomforestalgorithmforimprovedcollusiveinterestfloodingattack AT silinpeng mfrfadetectionapproachbasedonmultifeaturesandrandomforestalgorithmforimprovedcollusiveinterestfloodingattack AT wenzhifeng mfrfadetectionapproachbasedonmultifeaturesandrandomforestalgorithmforimprovedcollusiveinterestfloodingattack |