Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi...Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.展开更多
Snags are an important component of beech forests that promote biodiversity.However,their occurrence is completely marginal in managed stands.Creating snags in these stands would greatly enhance biodiversity.We invest...Snags are an important component of beech forests that promote biodiversity.However,their occurrence is completely marginal in managed stands.Creating snags in these stands would greatly enhance biodiversity.We investigated whether snag dimensions were important for saproxylic beetle richness since they were easily transferable parameters to forest management and assessed the presence of other snag microhabitats affecting beetle communities.Data collection was performed using passive flight traps placed on thirty snags in a recent beech reserve.A total of 6706 adults belonging to 231 saproxylic species(53 Red List species,23%)were captured.The results showed that the most important snag parameters were the diameter(thickness)and canopy openness of the surrounding stands.The occurrence of Fomes fomentarius,the volume of snag and decay class 3 were marginally significant in terms of the preference of all saproxylic species.Alpha diversity was reduced by an advanced degree of decay and a surprisingly deep stem cavity.After dividing snag thickness into categories(<35 cm;35–70 cm and>70 cm DBH),we found that categories with snag diameter greater than 35 cm showed little differences in all saproxylic and Red List species richness and diversity indices and exhibited the highest similarity in beetle communities.Regarding recommendations to forest managers in terms of optimization and simplification of practical procedures,we suggest actively creating high stumps to act as snags greater than 35 cm in DBH diameter to promote biodiversity in beech management stands.展开更多
The Asian longhorned beetle(ALB),Anoplophora glabripennis,is a well-known stem borer with high polyphagous properties causing frequent outbreaks in northeast China.An attractant-based trap is needed to improve the sen...The Asian longhorned beetle(ALB),Anoplophora glabripennis,is a well-known stem borer with high polyphagous properties causing frequent outbreaks in northeast China.An attractant-based trap is needed to improve the sensitivity,reliability,and effi ciency for detection of the beetle.In this study,the eff ects of attractants,trap types and color synergy of a trapping system were evaluated.Attractant blends comprised of the male-produced,two-component pheromone plus plant volatiles were used in the fi eld in Hengshui city.Plant volatiles(e.g.,1-pentanol,and 2-pentanol)in combination with male pheromones increased the mean number of trapped ALB compared to the pheromone alone.Males responded better than females to traps baited with plant volatiles alone,whereas traps emitting plant volatiles plus pheromone,regardless of trap type,captured more females than males.The ALB-trapping effi ciency of a modifi ed fl ight intercept panel trap was more than ten times as high as a woodborer panel trap and 1.2 times a fl ight intercept panel trap.The 1-pentanol and 2-pentanol attractants alone or in combination with male-produced pheromone were more eff ective for monitoring ALB than common lures.In laboratory Y-tube olfactometer experiments,the color brown was better at increasing attraction of both males and females to 1-pentanol,2-pentanol,1-pentanol+pheromone,and 2-pentanol+pheromone compared to the clear-glass control arm.The fi ndings provide a reliable and eff ective trap system to monitor ALB infestations.展开更多
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.
基金This research was supported by grant No.QK23020008,funded by the Ministry of Agriculture of the Czech Republic.
文摘Snags are an important component of beech forests that promote biodiversity.However,their occurrence is completely marginal in managed stands.Creating snags in these stands would greatly enhance biodiversity.We investigated whether snag dimensions were important for saproxylic beetle richness since they were easily transferable parameters to forest management and assessed the presence of other snag microhabitats affecting beetle communities.Data collection was performed using passive flight traps placed on thirty snags in a recent beech reserve.A total of 6706 adults belonging to 231 saproxylic species(53 Red List species,23%)were captured.The results showed that the most important snag parameters were the diameter(thickness)and canopy openness of the surrounding stands.The occurrence of Fomes fomentarius,the volume of snag and decay class 3 were marginally significant in terms of the preference of all saproxylic species.Alpha diversity was reduced by an advanced degree of decay and a surprisingly deep stem cavity.After dividing snag thickness into categories(<35 cm;35–70 cm and>70 cm DBH),we found that categories with snag diameter greater than 35 cm showed little differences in all saproxylic and Red List species richness and diversity indices and exhibited the highest similarity in beetle communities.Regarding recommendations to forest managers in terms of optimization and simplification of practical procedures,we suggest actively creating high stumps to act as snags greater than 35 cm in DBH diameter to promote biodiversity in beech management stands.
基金supported by the Fundamental Research Funds for the Central Universities(2572021BK01).
文摘The Asian longhorned beetle(ALB),Anoplophora glabripennis,is a well-known stem borer with high polyphagous properties causing frequent outbreaks in northeast China.An attractant-based trap is needed to improve the sensitivity,reliability,and effi ciency for detection of the beetle.In this study,the eff ects of attractants,trap types and color synergy of a trapping system were evaluated.Attractant blends comprised of the male-produced,two-component pheromone plus plant volatiles were used in the fi eld in Hengshui city.Plant volatiles(e.g.,1-pentanol,and 2-pentanol)in combination with male pheromones increased the mean number of trapped ALB compared to the pheromone alone.Males responded better than females to traps baited with plant volatiles alone,whereas traps emitting plant volatiles plus pheromone,regardless of trap type,captured more females than males.The ALB-trapping effi ciency of a modifi ed fl ight intercept panel trap was more than ten times as high as a woodborer panel trap and 1.2 times a fl ight intercept panel trap.The 1-pentanol and 2-pentanol attractants alone or in combination with male-produced pheromone were more eff ective for monitoring ALB than common lures.In laboratory Y-tube olfactometer experiments,the color brown was better at increasing attraction of both males and females to 1-pentanol,2-pentanol,1-pentanol+pheromone,and 2-pentanol+pheromone compared to the clear-glass control arm.The fi ndings provide a reliable and eff ective trap system to monitor ALB infestations.
