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
Aiming at the industry cyber-physical system(ICPS)where Denial-of-Service(DoS)attacks and actuator failure coexist,the integrated security control problem of ICPS under multi-objective constraints was studied.First,fr...Aiming at the industry cyber-physical system(ICPS)where Denial-of-Service(DoS)attacks and actuator failure coexist,the integrated security control problem of ICPS under multi-objective constraints was studied.First,from the perspective of the defender,according to the differential impact of the system under DoS attacks of different energies,the DoS attacks energy grading detection standard was formulated,and the ICPS comprehensive security control framework was constructed.Secondly,a security transmission strategy based on event triggering was designed.Under the DoS attack energy classification detection mechanism,for large-energy attacks,the method based on time series analysis was considered to predict and compensate for lost data.Therefore,on the basis of passive and elastic response to small energy attacks,the active defense capability against DoS attacks was increased.Then by introducing the conecomplement linearization algorithm,the calculation methods of the state and fault estimation observer and the integrated safety controller were deduced,the goal of DoS attack active and passive hybrid intrusion tolerance and actuator failure active fault tolerance were realized.Finally,a simulation example of a four-capacity water tank system was given to verify the validity of the obtained conclusions.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 Gansu Higher Education Innovation Fund Project(No.2023B-439)。
文摘Aiming at the industry cyber-physical system(ICPS)where Denial-of-Service(DoS)attacks and actuator failure coexist,the integrated security control problem of ICPS under multi-objective constraints was studied.First,from the perspective of the defender,according to the differential impact of the system under DoS attacks of different energies,the DoS attacks energy grading detection standard was formulated,and the ICPS comprehensive security control framework was constructed.Secondly,a security transmission strategy based on event triggering was designed.Under the DoS attack energy classification detection mechanism,for large-energy attacks,the method based on time series analysis was considered to predict and compensate for lost data.Therefore,on the basis of passive and elastic response to small energy attacks,the active defense capability against DoS attacks was increased.Then by introducing the conecomplement linearization algorithm,the calculation methods of the state and fault estimation observer and the integrated safety controller were deduced,the goal of DoS attack active and passive hybrid intrusion tolerance and actuator failure active fault tolerance were realized.Finally,a simulation example of a four-capacity water tank system was given to verify the validity of the obtained conclusions.
