An expanding human population and technological progress demand clean and effective energy-storing systems.Within the realm of energy-storing devices,supercapacitors(SCs)have grabbed huge focus owing to their high-pow...An expanding human population and technological progress demand clean and effective energy-storing systems.Within the realm of energy-storing devices,supercapacitors(SCs)have grabbed huge focus owing to their high-power density,unique cycling stability,and fast charging discharging capabilities.Electrode material has a prominent impact on the effectiveness of SCs.Several types of electrode materials have been used,encompassing varied metal oxides,activated carbon,conducting polymers,and MOFs.Metal organic frameworks(MOFs)are considered emerging electrode candidates,which could be ascribed to the tunable porosity,large surface areas,and designed morphology.This review shows a detailed analysis of various mono-,bi-,and tri-metallic MOFs along with derivatives in SC applications,their structural characteristics,and synthetic strategies.It also critically evaluates MOFs potential to boost the SC's energy density,power density,stability,and conductivity.Also,it underscores their significance in the establishment of future-oriented energy storage applications.展开更多
Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks...Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.展开更多
文摘An expanding human population and technological progress demand clean and effective energy-storing systems.Within the realm of energy-storing devices,supercapacitors(SCs)have grabbed huge focus owing to their high-power density,unique cycling stability,and fast charging discharging capabilities.Electrode material has a prominent impact on the effectiveness of SCs.Several types of electrode materials have been used,encompassing varied metal oxides,activated carbon,conducting polymers,and MOFs.Metal organic frameworks(MOFs)are considered emerging electrode candidates,which could be ascribed to the tunable porosity,large surface areas,and designed morphology.This review shows a detailed analysis of various mono-,bi-,and tri-metallic MOFs along with derivatives in SC applications,their structural characteristics,and synthetic strategies.It also critically evaluates MOFs potential to boost the SC's energy density,power density,stability,and conductivity.Also,it underscores their significance in the establishment of future-oriented energy storage applications.
文摘Distributed denial of service(DDoS)attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user.We proposed a deep neural network(DNN)model for the detection of DDoS attacks in the Software-Defined Networking(SDN)paradigm.SDN centralizes the control plane and separates it from the data plane.It simplifies a network and eliminates vendor specification of a device.Because of this open nature and centralized control,SDN can easily become a victim of DDoS attacks.We proposed a supervised Developed Deep Neural Network(DDNN)model that can classify the DDoS attack traffic and legitimate traffic.Our Developed Deep Neural Network(DDNN)model takes a large number of feature values as compared to previously proposed Machine Learning(ML)models.The proposed DNN model scans the data to find the correlated features and delivers high-quality results.The model enhances the security of SDN and has better accuracy as compared to previously proposed models.We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets.Our model results in a high accuracy rate of 99.76%with a low false-positive rate and 0.065%low loss rate.The accuracy increases to 99.80%as we increase the number of epochs to 100 rounds.Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models.It can handle a huge amount of structured and unstructured data and can easily solve complex problems.
