Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything
Vehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network eq...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/11/2563 |
| _version_ | 1798035177514991616 |
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| author | Jaehyung Wee Jin-Ghoo Choi Wooguil Pak |
| author_facet | Jaehyung Wee Jin-Ghoo Choi Wooguil Pak |
| author_sort | Jaehyung Wee |
| collection | DOAJ |
| description | Vehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network equipment significantly depends on the packet classification algorithm because it is one of the most fundamental packet processing functions. Thus, the algorithm should run fast even with the huge set of packet processing rules. Unfortunately, previous packet classification algorithms have focused on the processing speed only, failing to be scalable with the rule-set size. In this paper, we propose a new packet classification approach balancing classification speed and scalability. It can be applied to most decision tree-based packet classification algorithms such as HyperCuts and EffiCuts. It determines partitioning fields considering the rule duplication explicitly, which makes the algorithm memory-effective. In addition, the proposed approach reduces the decision tree size substantially with the minimal sacrifice of classification performance. As a result, we can attain high-speed packet classification and scalability simultaneously, which is very essential for latest services such as V2X and Internet-of-Things (IoT). |
| first_indexed | 2024-04-11T20:53:36Z |
| format | Article |
| id | doaj.art-4c6cf2d0d7424fcd95c772a86dd29ae8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:53:36Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4c6cf2d0d7424fcd95c772a86dd29ae82022-12-22T04:03:45ZengMDPI AGSensors1424-82202019-06-011911256310.3390/s19112563s19112563Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-EverythingJaehyung Wee0Jin-Ghoo Choi1Wooguil Pak2Computer Engineering Department, Keimyung University, Daegu 42601, KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaVehicle-to-Everything (V2X) requires high-speed communication and high-level security. However, as the number of connected devices increases exponentially, communication networks are suffering from huge traffic and various security issues. It is well known that performance and security of network equipment significantly depends on the packet classification algorithm because it is one of the most fundamental packet processing functions. Thus, the algorithm should run fast even with the huge set of packet processing rules. Unfortunately, previous packet classification algorithms have focused on the processing speed only, failing to be scalable with the rule-set size. In this paper, we propose a new packet classification approach balancing classification speed and scalability. It can be applied to most decision tree-based packet classification algorithms such as HyperCuts and EffiCuts. It determines partitioning fields considering the rule duplication explicitly, which makes the algorithm memory-effective. In addition, the proposed approach reduces the decision tree size substantially with the minimal sacrifice of classification performance. As a result, we can attain high-speed packet classification and scalability simultaneously, which is very essential for latest services such as V2X and Internet-of-Things (IoT).https://www.mdpi.com/1424-8220/19/11/2563network securitypacket classificationhigh-speed communicationhigh scalability |
| spellingShingle | Jaehyung Wee Jin-Ghoo Choi Wooguil Pak Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything Sensors network security packet classification high-speed communication high scalability |
| title | Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything |
| title_full | Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything |
| title_fullStr | Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything |
| title_full_unstemmed | Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything |
| title_short | Wildcard Fields-Based Partitioning for Fast and Scalable Packet Classification in Vehicle-to-Everything |
| title_sort | wildcard fields based partitioning for fast and scalable packet classification in vehicle to everything |
| topic | network security packet classification high-speed communication high scalability |
| url | https://www.mdpi.com/1424-8220/19/11/2563 |
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