Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network...Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.展开更多
Modem network security devices employ packet classification and pattern matching algorithms to inspect packets. Due to the complexity and heterogeneity of different search data structures, it is difficult for existing...Modem network security devices employ packet classification and pattern matching algorithms to inspect packets. Due to the complexity and heterogeneity of different search data structures, it is difficult for existing algorithms to leverage modern hardware platforms to achieve high performance. This paper presents a Structural Compression （SC） method that optimizes the data structures of both algorithms. It reviews both algorithms under the model of search space decomposition, and homogenizes their search data structures. This approach not only guarantees deterministic lookup speed but also optimizes the data structure for efficient implementation oi1 many-core platforms. The performance evaluation reveals that the homogeneous data structure achieves 10Gbps line-rate 64byte packet classification throughput and multi-Gbps deep inspection speed.展开更多
A transmission bottleneck occurs during each human immunodeficiency virus(HIV) transmission event, which allows only a few viruses to establish new infection. However, the genetic characteristics of the transmitted vi...A transmission bottleneck occurs during each human immunodeficiency virus(HIV) transmission event, which allows only a few viruses to establish new infection. However, the genetic characteristics of the transmitted viruses that are preferentially selected have not been fully elucidated. Here, we analyzed amino acids changes in the envelope protein during simian immunodeficiency virus(SIV)/HIV deep transmission history and current HIV evolution within the last 15–20 years. Our results confirmed that the V1V2 region of gp120 protein, particularly V1, was preferentially selected. A shorter V1 region was preferred during transmission history, while during epidemic, HIV may evolve to an expanded V1 region gradually and thus escape immune recognition. We then constructed different HIV-1 V1 mutants using different HIV-1 subtypes to elucidate the role of the V1 region in envelope function. We found that the V1 region, although highly variable, was indispensable for virus entry and infection, probably because V1 deletion mutants exhibited impaired processing of gp160 into mature gp120 and gp41. Additionally, the V1 region affected Env incorporation. These results indicated that the V1 region played a critical role in HIV transmission and infection.展开更多
For a quantized enveloping algebra of finite type, one can associate a natural monomial to a dominant weight. We show that these monomials for types A5 and D4 are semitight(i.e., a Z-linear combination of elements in ...For a quantized enveloping algebra of finite type, one can associate a natural monomial to a dominant weight. We show that these monomials for types A5 and D4 are semitight(i.e., a Z-linear combination of elements in the canonical basis) by a direct calculation.展开更多
基金Project(2007CB311106) supported by National Key Basic Research Program of ChinaProject(NEUL20090101) supported by the Foundation of National Information Control Laboratory of China
文摘Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.
文摘Modem network security devices employ packet classification and pattern matching algorithms to inspect packets. Due to the complexity and heterogeneity of different search data structures, it is difficult for existing algorithms to leverage modern hardware platforms to achieve high performance. This paper presents a Structural Compression （SC） method that optimizes the data structures of both algorithms. It reviews both algorithms under the model of search space decomposition, and homogenizes their search data structures. This approach not only guarantees deterministic lookup speed but also optimizes the data structure for efficient implementation oi1 many-core platforms. The performance evaluation reveals that the homogeneous data structure achieves 10Gbps line-rate 64byte packet classification throughput and multi-Gbps deep inspection speed.
基金supported by the International Science & Technology Cooperation Program of China (2011DFA31030)Deutsche Forschungsgemeinschaft (Transregio TRR60),National Natural Science Foundation of China (No.81461130019)
文摘A transmission bottleneck occurs during each human immunodeficiency virus(HIV) transmission event, which allows only a few viruses to establish new infection. However, the genetic characteristics of the transmitted viruses that are preferentially selected have not been fully elucidated. Here, we analyzed amino acids changes in the envelope protein during simian immunodeficiency virus(SIV)/HIV deep transmission history and current HIV evolution within the last 15–20 years. Our results confirmed that the V1V2 region of gp120 protein, particularly V1, was preferentially selected. A shorter V1 region was preferred during transmission history, while during epidemic, HIV may evolve to an expanded V1 region gradually and thus escape immune recognition. We then constructed different HIV-1 V1 mutants using different HIV-1 subtypes to elucidate the role of the V1 region in envelope function. We found that the V1 region, although highly variable, was indispensable for virus entry and infection, probably because V1 deletion mutants exhibited impaired processing of gp160 into mature gp120 and gp41. Additionally, the V1 region affected Env incorporation. These results indicated that the V1 region played a critical role in HIV transmission and infection.
文摘For a quantized enveloping algebra of finite type, one can associate a natural monomial to a dominant weight. We show that these monomials for types A5 and D4 are semitight(i.e., a Z-linear combination of elements in the canonical basis) by a direct calculation.
