Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecolog...The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecological civilization construction,achieve the carbon emission peak and carbon neutrality target,and enhance global climate governance capabilities.This study first proposes the existing classification outline of the technology promotion lists,technology demand lists,and future technology lists.Then,different methodologies are integrated on the basis of the existing outline of four technology lists:China’s existing technological promotion list for addressing climate change,China’s demand list for climate change mitigation technology,China’s key technology list for addressing climate change,and China’s future technology list for addressing climate change.What’s more,core technologies are analyzed in the aspects of technology maturity,carbon reduction cost,carbon reduction potential,economic benefits,social influence,uncertainty,etc.The results show that:key industries and sectors in China already have relatively mature mitigation/adaptation technologies to support the achievement of climate change targets.The multi-sectoral system of promoting climate friendly technologies has been established,which has played an active role in tackling climate change.Currently,climate technology needs are concentrated in the traditional technology and equipment upgrading,renewable energy technology,and management decision-making support technology.The key technologies are concentrated in 3 major areas and 12 technological directions that urgently need a breakthrough.For carbon emmission peak and nentrality,carbon depth reduction and zero carbon emissions and geoengineering technology(CDR and SRM)have played an important role in forming the structure of global emissions and achieving carbon neutrality in the future.Thus,the uncertainty assessment for the comprehensive technology cost effectiveness,technology integration direction,technical maturity,ethics and ecological impacts is supportive to the national technology strategy.Finally,the presented study proposes several policy implications for medium-and long-term technology deployment,improving technology conversion rate,promoting the research and development of core technologies,and forming a technology list collaborative update and release mechanism.展开更多
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
基金Special Programm for Compiling the Fourth National Assessment Report on Climate Change of the Ministry of Science and Technology.
文摘The compilation of technology lists addressing climate change has a guiding effect on promoting technological research and development,demonstration,and popularization.It is also crucial for China to strengthen ecological civilization construction,achieve the carbon emission peak and carbon neutrality target,and enhance global climate governance capabilities.This study first proposes the existing classification outline of the technology promotion lists,technology demand lists,and future technology lists.Then,different methodologies are integrated on the basis of the existing outline of four technology lists:China’s existing technological promotion list for addressing climate change,China’s demand list for climate change mitigation technology,China’s key technology list for addressing climate change,and China’s future technology list for addressing climate change.What’s more,core technologies are analyzed in the aspects of technology maturity,carbon reduction cost,carbon reduction potential,economic benefits,social influence,uncertainty,etc.The results show that:key industries and sectors in China already have relatively mature mitigation/adaptation technologies to support the achievement of climate change targets.The multi-sectoral system of promoting climate friendly technologies has been established,which has played an active role in tackling climate change.Currently,climate technology needs are concentrated in the traditional technology and equipment upgrading,renewable energy technology,and management decision-making support technology.The key technologies are concentrated in 3 major areas and 12 technological directions that urgently need a breakthrough.For carbon emmission peak and nentrality,carbon depth reduction and zero carbon emissions and geoengineering technology(CDR and SRM)have played an important role in forming the structure of global emissions and achieving carbon neutrality in the future.Thus,the uncertainty assessment for the comprehensive technology cost effectiveness,technology integration direction,technical maturity,ethics and ecological impacts is supportive to the national technology strategy.Finally,the presented study proposes several policy implications for medium-and long-term technology deployment,improving technology conversion rate,promoting the research and development of core technologies,and forming a technology list collaborative update and release mechanism.
