In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper p...In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile, SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising time. The proposed network effectively addresses issues associated with the loss of detail information.展开更多
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
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile, SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising time. The proposed network effectively addresses issues associated with the loss of detail information.
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.
