In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prep...In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.展开更多
Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon neutrality.Toward real-world applications,howev...Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon neutrality.Toward real-world applications,however,the parasitic heat input and heat leakage pose crucial challenges to commercial and residential buildings cooling.The integrating of radiative cooling and thermal insulation properties represents an attractive direction in renewable energy-efficient building envelope materials.Herein,we present a hierarchically porous hybrid film as a scalable and flexible thermal insulating subambient radiative cooler via a simple and inexpensive inverse high internal phase emulsion strategy.The as-prepared porous hybrid film exhibits an intrinsic combination of high solar reflectance(0.95),strong longwave infrared thermal emittance(0.97),and low thermal conductivity(31 mW/(m K)),yielding a subambient cooling temperature of~8.4℃ during the night and~6.5℃ during the hot midday with an average cooling power of~94 W/m^(2) under a solar intensity of~900 W/m^(2).Promisingly,combining the superhydrophobicity,durability,superelasticity,robust mechanical strength,and industrial applicability,the film is favorable for large-scale,sustainable and energy-saving applications in a wide variety of climates and complicated surfaces,enabling a substantial reduction of energy costs,greenhouse gas emission and associated ozone-depleting from traditional cooling systems.展开更多
文摘In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.
基金This work was supported by the National Key Research and Development Program of China(2017YFA0204600)the National Natural Science Foundation of China(51721002 and 52033003)+4 种基金Y.Z.acknowledges the support by the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learningthe National Natural Science Foundation of China(62175154)Shanghai Pujiang Program(20PJ1411900)Shanghai Science and Technology Program(21ZR1445500)T.W.acknowledges the support of Shanghai Yangfan Program(22YF1430200).
文摘Passive daytime radiative cooling(PDRC)is one of the promising alternatives to electrical cooling and has a significant impact on worldwide energy consumption and carbon neutrality.Toward real-world applications,however,the parasitic heat input and heat leakage pose crucial challenges to commercial and residential buildings cooling.The integrating of radiative cooling and thermal insulation properties represents an attractive direction in renewable energy-efficient building envelope materials.Herein,we present a hierarchically porous hybrid film as a scalable and flexible thermal insulating subambient radiative cooler via a simple and inexpensive inverse high internal phase emulsion strategy.The as-prepared porous hybrid film exhibits an intrinsic combination of high solar reflectance(0.95),strong longwave infrared thermal emittance(0.97),and low thermal conductivity(31 mW/(m K)),yielding a subambient cooling temperature of~8.4℃ during the night and~6.5℃ during the hot midday with an average cooling power of~94 W/m^(2) under a solar intensity of~900 W/m^(2).Promisingly,combining the superhydrophobicity,durability,superelasticity,robust mechanical strength,and industrial applicability,the film is favorable for large-scale,sustainable and energy-saving applications in a wide variety of climates and complicated surfaces,enabling a substantial reduction of energy costs,greenhouse gas emission and associated ozone-depleting from traditional cooling systems.
