The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptibl...The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptible to security threats.One significant risk to cloud networks is Distributed Denial-of-Service(DoS)attacks,where attackers aim to overcome a target system with excessive data and requests.Among these,low-rate DoS(LR-DoS)attacks present a particular challenge to detection.By sending bursts of attacks at irregular intervals,LR-DoS significantly degrades the targeted system’s Quality of Service(QoS).The low-rate nature of these attacks confuses their detection,as they frequently trigger congestion control mechanisms,leading to significant instability in IoT systems.Therefore,to detect the LR-DoS attack,an innovative deep-learning model has been developed for this research work.The standard dataset is utilized to collect the required data.Further,the deep feature extraction process is executed using the Residual Autoencoder with Sparse Attention(ResAE-SA),which helps derive the significant feature required for detection.Ultimately,the Adaptive Dense Recurrent Neural Network(ADRNN)is implemented to detect LR-DoS effectively.To enhance the detection process,the parameters present in the ADRNN are optimized using the Renovated Random Attribute-based Fennec Fox Optimization(RRA-FFA).The proposed optimization reduces the False Discovery Rate and False Positive Rate,maximizing the Matthews Correlation Coefficient from 23,70.8,76.2,84.28 in Dataset 1 and 70.28,73.8,74.1,82.6 in Dataset 2 on EPC-ADRNN,DPO-ADRNN,GTO-ADRNN,FFA-ADRNN respectively to 95.8 on Dataset 1 and 91.7 on Dataset 2 in proposed model.At batch size 4,the accuracy of the designed RRA-FFA-ADRNN model progressed by 9.2%to GTO-ADRNN,11.6%to EFC-ADRNN,10.9%to DPO-ADRNN,and 4%to FFA-ADRNN for Dataset 1.The accuracy of the proposed RRA-FFA-ADRNN is boosted by 12.9%,9.09%,11.6%,and 10.9%over FFCNN,SVM,RNN,and DRNN,using Dataset 2,showing a better improvement in accuracy with that of the proposed RRA-FFA-ADRNN model with 95.7%using Dataset 1 and 94.1%with Dataset 2,which is better than the existing baseline models.展开更多
Foxes are susceptible to SARS-CoV-2 in laboratory settings,and there have also been reports of natural infections of both SARS-CoV and SARS-CoV-2 in foxes.In this study,we assessed the binding capacities of fox ACE2 t...Foxes are susceptible to SARS-CoV-2 in laboratory settings,and there have also been reports of natural infections of both SARS-CoV and SARS-CoV-2 in foxes.In this study,we assessed the binding capacities of fox ACE2 to important sarbecoviruses,including SARS-CoV,SARS-CoV-2,and animal-origin SARS-CoV-2 related viruses.Our findings demonstrated that fox ACE2 exhibits broad binding capabilities to receptor-binding domains(RBDs)of sarbecoviruses.We further determined the cryo-EM structures of fox ACE2 complexed with RBDs of SARS-CoV,SARS-CoV-2 prototype(PT),and Omicron BF.7.Through structural analysis,we identified that the K417 mutation can weaken the ability of SARS-CoV-2 sub-variants to bind to fox ACE2,thereby reducing the susceptibility of foxes to SARS-CoV-2 sub-variants.In addition,the Y498 residue in the SARS-CoV RBD plays a crucial role in forming a vital cation-πinteraction with K353 in the fox ACE2 receptor.This interaction is the primary determinant for the higher affinity of the SARS-CoV RBD compared to that of the SARS-CoV-2 PT RBD.These results indicate that foxes serve as potential hosts for numerous sarbecoviruses,highlighting the critical importance of surveillance efforts.展开更多
基金funded by the Ministry of Higher Education Malaysia,Fundamental Research Grant Scheme(FRGS),FRGS/1/2024/ICT07/UPNM/02/1.
文摘The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptible to security threats.One significant risk to cloud networks is Distributed Denial-of-Service(DoS)attacks,where attackers aim to overcome a target system with excessive data and requests.Among these,low-rate DoS(LR-DoS)attacks present a particular challenge to detection.By sending bursts of attacks at irregular intervals,LR-DoS significantly degrades the targeted system’s Quality of Service(QoS).The low-rate nature of these attacks confuses their detection,as they frequently trigger congestion control mechanisms,leading to significant instability in IoT systems.Therefore,to detect the LR-DoS attack,an innovative deep-learning model has been developed for this research work.The standard dataset is utilized to collect the required data.Further,the deep feature extraction process is executed using the Residual Autoencoder with Sparse Attention(ResAE-SA),which helps derive the significant feature required for detection.Ultimately,the Adaptive Dense Recurrent Neural Network(ADRNN)is implemented to detect LR-DoS effectively.To enhance the detection process,the parameters present in the ADRNN are optimized using the Renovated Random Attribute-based Fennec Fox Optimization(RRA-FFA).The proposed optimization reduces the False Discovery Rate and False Positive Rate,maximizing the Matthews Correlation Coefficient from 23,70.8,76.2,84.28 in Dataset 1 and 70.28,73.8,74.1,82.6 in Dataset 2 on EPC-ADRNN,DPO-ADRNN,GTO-ADRNN,FFA-ADRNN respectively to 95.8 on Dataset 1 and 91.7 on Dataset 2 in proposed model.At batch size 4,the accuracy of the designed RRA-FFA-ADRNN model progressed by 9.2%to GTO-ADRNN,11.6%to EFC-ADRNN,10.9%to DPO-ADRNN,and 4%to FFA-ADRNN for Dataset 1.The accuracy of the proposed RRA-FFA-ADRNN is boosted by 12.9%,9.09%,11.6%,and 10.9%over FFCNN,SVM,RNN,and DRNN,using Dataset 2,showing a better improvement in accuracy with that of the proposed RRA-FFA-ADRNN model with 95.7%using Dataset 1 and 94.1%with Dataset 2,which is better than the existing baseline models.
基金supported by the National Key R&D Program of China(2022YFC2303401,2021YFA1300803)National Natural Science Foundation of China(32122008)+2 种基金supported by Young Elite Scientists Sponsorship Program by CAST(2021QNRC001)fellowships from the China Postdoctoral Science Foundation(2022T150688)the Postdoctoral Science Foundation of China(2021M700161).
文摘Foxes are susceptible to SARS-CoV-2 in laboratory settings,and there have also been reports of natural infections of both SARS-CoV and SARS-CoV-2 in foxes.In this study,we assessed the binding capacities of fox ACE2 to important sarbecoviruses,including SARS-CoV,SARS-CoV-2,and animal-origin SARS-CoV-2 related viruses.Our findings demonstrated that fox ACE2 exhibits broad binding capabilities to receptor-binding domains(RBDs)of sarbecoviruses.We further determined the cryo-EM structures of fox ACE2 complexed with RBDs of SARS-CoV,SARS-CoV-2 prototype(PT),and Omicron BF.7.Through structural analysis,we identified that the K417 mutation can weaken the ability of SARS-CoV-2 sub-variants to bind to fox ACE2,thereby reducing the susceptibility of foxes to SARS-CoV-2 sub-variants.In addition,the Y498 residue in the SARS-CoV RBD plays a crucial role in forming a vital cation-πinteraction with K353 in the fox ACE2 receptor.This interaction is the primary determinant for the higher affinity of the SARS-CoV RBD compared to that of the SARS-CoV-2 PT RBD.These results indicate that foxes serve as potential hosts for numerous sarbecoviruses,highlighting the critical importance of surveillance efforts.
