With the development of computer technology, network bandwidth and network traffic continue to increase. Considering the large data flow, it is imperative to perform inspection effectively on network packets. In order...With the development of computer technology, network bandwidth and network traffic continue to increase. Considering the large data flow, it is imperative to perform inspection effectively on network packets. In order to find a solution of deep packet inspection which can appropriate to the current network environment, this paper built a deep packet inspection system based on many-core platform, and in this way, verified the feasibility to implement a deep packet inspection system under many-core platform with both high performance and low consumption. After testing and analysis of the system performance, it has been found that the deep packet inspection based on many-core platform TILE_Gx36 [1] [2] can process network traffic of which the bandwidth reaches up to 4 Gbps. To a certain extent, the performance has improved compared to most deep packet inspection system based on X86 platform at present.展开更多
The accurate and efficient classification of Internet traffic is the first and key step to ac-curate traffic management,network security and traffic analysis. The classic ways to identify flows is either inaccurate or...The accurate and efficient classification of Internet traffic is the first and key step to ac-curate traffic management,network security and traffic analysis. The classic ways to identify flows is either inaccurate or inefficient,which are not suitable to be applied to real-time online classification. In this paper,we originally presented an early recognition method named Early Recognition Based on Deep Packet Inspection (ERBDPI) based on deep packet inspection,after analyzing the distribution of payload signature between packets of a flow in detail. The basic concept of ERBDPI is classifying flows based on the payload signature of their first some packets,so that we can identify traffic at the be-ginning of a flow connection. We compared the performance of ERBDPI with that of traditional sampling methods both synthetically and using real-world traffic traces. The result shows that ERBDPI can get a higher classification accuracy with a lower packet sampling rate,which makes it suitable to be applied to accurate real-time classification in high-speed links.展开更多
提出一种基于确定的有穷状态自动机(deterministic finite automaton,简称DFA)的正则表达式压缩算法.首先,定义了膨胀率DR(distending rate)来描述正则表达式的膨胀特性.然后基于DR提出一种分片的算法RECCADR(regular expressions cut a...提出一种基于确定的有穷状态自动机(deterministic finite automaton,简称DFA)的正则表达式压缩算法.首先,定义了膨胀率DR(distending rate)来描述正则表达式的膨胀特性.然后基于DR提出一种分片的算法RECCADR(regular expressions cut and combine algorithm based on DR),有效地选择出导致DFA状态膨胀的片段并隔离,降低了单个正则表达式存储需求.同时,基于正则表达式的组合关系提出一种选择性分群算法REGADR(regular expressions group algorithm based on DR),在可以接受的存储需求总量下,通过选择性分群大幅度减少了状态机的个数,有效地降低了匹配算法的复杂性.展开更多
文摘With the development of computer technology, network bandwidth and network traffic continue to increase. Considering the large data flow, it is imperative to perform inspection effectively on network packets. In order to find a solution of deep packet inspection which can appropriate to the current network environment, this paper built a deep packet inspection system based on many-core platform, and in this way, verified the feasibility to implement a deep packet inspection system under many-core platform with both high performance and low consumption. After testing and analysis of the system performance, it has been found that the deep packet inspection based on many-core platform TILE_Gx36 [1] [2] can process network traffic of which the bandwidth reaches up to 4 Gbps. To a certain extent, the performance has improved compared to most deep packet inspection system based on X86 platform at present.
基金Supported by grant from the Major State Basic Research Development Program of China (No.2007CB307102)
文摘The accurate and efficient classification of Internet traffic is the first and key step to ac-curate traffic management,network security and traffic analysis. The classic ways to identify flows is either inaccurate or inefficient,which are not suitable to be applied to real-time online classification. In this paper,we originally presented an early recognition method named Early Recognition Based on Deep Packet Inspection (ERBDPI) based on deep packet inspection,after analyzing the distribution of payload signature between packets of a flow in detail. The basic concept of ERBDPI is classifying flows based on the payload signature of their first some packets,so that we can identify traffic at the be-ginning of a flow connection. We compared the performance of ERBDPI with that of traditional sampling methods both synthetically and using real-world traffic traces. The result shows that ERBDPI can get a higher classification accuracy with a lower packet sampling rate,which makes it suitable to be applied to accurate real-time classification in high-speed links.
文摘提出一种基于确定的有穷状态自动机(deterministic finite automaton,简称DFA)的正则表达式压缩算法.首先,定义了膨胀率DR(distending rate)来描述正则表达式的膨胀特性.然后基于DR提出一种分片的算法RECCADR(regular expressions cut and combine algorithm based on DR),有效地选择出导致DFA状态膨胀的片段并隔离,降低了单个正则表达式存储需求.同时,基于正则表达式的组合关系提出一种选择性分群算法REGADR(regular expressions group algorithm based on DR),在可以接受的存储需求总量下,通过选择性分群大幅度减少了状态机的个数,有效地降低了匹配算法的复杂性.
