As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditio...As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.展开更多
Length composition analysis can provide insights into the dynamics of a fish population.Accurate quantification of the size structure of a population is critical to understand the status of a fishery and how the popul...Length composition analysis can provide insights into the dynamics of a fish population.Accurate quantification of the size structure of a population is critical to understand the status of a fishery and how the population responds to environmental stressors.A scientific observer program is a reliable way to provide such accurate information.However,100%observer coverage is usually impossible for most fisheries because of logistic and financial constraints.Thus,there is a need to evaluate observer program performance,identify suitable sample sizes,and optimize the allocation of observation efforts.The objective of this study is to evaluate the effects of sample size on the quality of length composition data and identify an optimal coverage rate and observation ratio to improve the observation efficiency using an onboard observer data set from China's tuna longline fishery in the western and central Pacific Ocean.We found that the required sample size varies with fish species,indices used to describe length composition,the acceptable accuracy of the estimates,and the allocation methods of sampling effort.Ignoring other information requirements,1000 individuals would be sufficient for most species to reliably quantify length compositions,and a smaller sample size could generate reliable estimates of mean length.A coverage rate of 20%would be sufficient for most species,but a lower coverage rate(5%or 10%)could also be effective to meet with the accuracy and precision requirement in estimating length compositions.A nonrandom effort allocation among fishing baskets within a set could cause the length composition to be overestimated or underestimated for some species.The differences in effective sample sizes among species should be included in the consideration for a rational allocation of observation effort among species when there are different species management priorities.展开更多
To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang P...To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang Province of China. The hypocotyl inoculation method was used to characterize the virulence of P. sojae on 13 differential cultivars, and the amplified fragment length polymorphism（AFLP） technique was used to analyze difference in the genetic structure of P. sojae. The results indicated that an abundant diversity of genetic structures and pathotypes of P. sojae, a more uniform distribution of pathotypes and less dominance of pathotypes occurred in corn-soybean and wheat-soybean rotation fields than in a continuous soybean mono-cropping field. These findings suggested that P. sojae did not easily become the dominant race in rotation fields, which maintain disease resistance in soybean varieties. Therefore, the results of this study suggested that Phytophthora stem and root rot of soybeans could be effectively controlled by rotating soybeans with non-host crops of corn and wheat.展开更多
基金supported by the General Program of the National Natural Science Foundation of China under Grant No.62172093the National Key R&D Program of China under Grant No.2018YFB1800602+1 种基金2019 Industrial Internet Innovation and Development Project,Ministry of Industry and Information Technology(MIIT)under Grant No.6709010003Ministry of Education-China Mobile Research Fund under Grant No.MCM20180506。
文摘As an essential function of encrypted Internet traffic analysis,encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision.However,the traditional plaintext-based Deep Packet Inspection(DPI)method cannot be applied to such a classification.Moreover,machine learning-based existing methods encounter two problems during feature selection:complex feature overcost processing and Transport Layer Security(TLS)version discrepancy.In this paper,we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit(multiPDU)length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment.Control experiments show that both Length-Sensitive(LS)composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance.Owing to faster feature extraction,our method is suitable for actual network environments and superior to state-of-the-art methods.
基金The work was supported by the scientific observer program of the distant-water fishery of the Agriculture Ministry of China(08–25).
文摘Length composition analysis can provide insights into the dynamics of a fish population.Accurate quantification of the size structure of a population is critical to understand the status of a fishery and how the population responds to environmental stressors.A scientific observer program is a reliable way to provide such accurate information.However,100%observer coverage is usually impossible for most fisheries because of logistic and financial constraints.Thus,there is a need to evaluate observer program performance,identify suitable sample sizes,and optimize the allocation of observation efforts.The objective of this study is to evaluate the effects of sample size on the quality of length composition data and identify an optimal coverage rate and observation ratio to improve the observation efficiency using an onboard observer data set from China's tuna longline fishery in the western and central Pacific Ocean.We found that the required sample size varies with fish species,indices used to describe length composition,the acceptable accuracy of the estimates,and the allocation methods of sampling effort.Ignoring other information requirements,1000 individuals would be sufficient for most species to reliably quantify length compositions,and a smaller sample size could generate reliable estimates of mean length.A coverage rate of 20%would be sufficient for most species,but a lower coverage rate(5%or 10%)could also be effective to meet with the accuracy and precision requirement in estimating length compositions.A nonrandom effort allocation among fishing baskets within a set could cause the length composition to be overestimated or underestimated for some species.The differences in effective sample sizes among species should be included in the consideration for a rational allocation of observation effort among species when there are different species management priorities.
基金Supported by the Special Fund for Agro-scientific Research in the Public Interest(201303018)the National Natural Science Foundation of China(31370449)
文摘To estimate the impact of crop rotation on the pathotype and genetic structure of Phythophthora sojae in fields, 372 isolates of P. sojae were obtained from long-term localisation experimental fields in Heilongjiang Province of China. The hypocotyl inoculation method was used to characterize the virulence of P. sojae on 13 differential cultivars, and the amplified fragment length polymorphism（AFLP） technique was used to analyze difference in the genetic structure of P. sojae. The results indicated that an abundant diversity of genetic structures and pathotypes of P. sojae, a more uniform distribution of pathotypes and less dominance of pathotypes occurred in corn-soybean and wheat-soybean rotation fields than in a continuous soybean mono-cropping field. These findings suggested that P. sojae did not easily become the dominant race in rotation fields, which maintain disease resistance in soybean varieties. Therefore, the results of this study suggested that Phytophthora stem and root rot of soybeans could be effectively controlled by rotating soybeans with non-host crops of corn and wheat.
