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
目的探讨Fox M1、Cep55及c-Myc蛋白在基底细胞样型乳腺癌(BLBC)中的表达及临床意义。方法采用免疫组化方法检测66例BLBC、70例NON-BLBC和66例癌旁正常乳腺组织中Fox M1、Cep55及c-Myc蛋白的表达情况及三者间的相互关系。结果 Fox M1蛋白...目的探讨Fox M1、Cep55及c-Myc蛋白在基底细胞样型乳腺癌(BLBC)中的表达及临床意义。方法采用免疫组化方法检测66例BLBC、70例NON-BLBC和66例癌旁正常乳腺组织中Fox M1、Cep55及c-Myc蛋白的表达情况及三者间的相互关系。结果 Fox M1蛋白在BLBC、NON-BLBC和癌旁正常乳腺组织中的阳性表达率分别为77.3%(51/66)、60.0%(42/70)、13.6%(9/66),Cep55蛋白在BLBC、NON-BLBC和癌旁正常乳腺组织中的阳性表达率分别为74.2%(49/66)、57.1%(40/70)、16.7%(11/66),c-Myc蛋白在BLBC、NON-BLBC和癌旁正常乳腺组织中的阳性表达率分别为71.2%(47/66)、54.3%(38/70)、22.7%(15/66),差异均有统计学意义(P<0.05)。Fox M1、Cep55及c-Myc蛋白的表达与BLBC的TNM分期及淋巴结转移情况密切相关(P<0.05),而与年龄、绝经与否、肿块大小无关(P>0.05)。Fox M1和Cep55蛋白在BLBC中的表达呈正相关关系(P<0.05),Fox M1和c-Myc蛋白在BLBC中的表达呈正相关关系(P<0.05),而Cep55与c-Myc的表达没有相关性(P>0.05)。结论 Fox M1、Cep55及c-Myc蛋白可能参与了BLBC的发生、发展,并且Fox M1分别与Cep55及c-Myc在BLBC发生发展过程中可能有一定的协同作用;Cep55蛋白与c-Myc蛋白在BLBC中的表达没有相关性,说明Cep55及c-Myc可能通过不同的作用对BLBC的发生发展过程产生影响。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.
