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
The study on hot deformation behavior of 316LN stainless steel at low-rate strain is very seldom at present. By sam-pling from industrial 316LN stainless steel and carrying out a compression heat deformation test by m...The study on hot deformation behavior of 316LN stainless steel at low-rate strain is very seldom at present. By sam-pling from industrial 316LN stainless steel and carrying out a compression heat deformation test by means of a Gleeble-3800 thermal simulation testing machine at a strain rate of 3×10^-3s^-1 and a deformation temperature of 600 - 1 100℃, true stress- strain curves are obtained. By analyzing the true stress-strain curves and microscopic structure of the samples, it can be conclu-ded that 1 000℃ and slightly higher temperature are suitable for low-rate strain processing of 316LN stainless steel.展开更多
In frequency domain,the power spectrum of Low-rate denial of service(LDoS) attacks is totally spread into the spectrum of normal traffic.It is a challenging task to detect and filter LDoS attack flows from the normal ...In frequency domain,the power spectrum of Low-rate denial of service(LDoS) attacks is totally spread into the spectrum of normal traffic.It is a challenging task to detect and filter LDoS attack flows from the normal traffic.Based on the analysis of LDoS attack flows and legitimate TCP traffic in time and frequency domains,the periodicity of the TCP traffic and LDoS attack flows is explored to facilitate the research of network traffic processing.Hence,an approach of LDoS attack flow filtering based on frequency spectrum analysis is proposed.In this approach,the TCP traffic and LDoS attack flows are transformed from the time domain into the frequency domain.Then the round-trip time(RTT) is estimated by using frequency domain search method.Analysis of amplitude spectrum shows that TCP traffic energy is mainly concentrated on the points of n/RTT.Therefore,a comb filter using infinite impulse response(IIR) filter is designed to filter out the LDoS attack flows in frequency domain,while most legitimate TCP traffic energy at the points of n/RTT are pass through.Experimental results show that the maximum pass rate for legitimate TCP traffic reaches 92.55%,while the maximum filtration rate of LDoS attack flows reaches 81.36%.The proposed approach can effectively filter the LDoS attack flows while less impact on the legitimate TCP traffic.展开更多
As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics ...As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics of a low average rate and concealment.With features extracted from the network traffic,a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper.An attack feature set containing the Acknowledge character(ACK)sequence number,the packet size,and the queue length is used to classify normal and LDoS attack traffics.Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor(KNN)classifier separately,and to obtain the decision contour matrix.Then a posteriori probability in the matrix is fused,and the fusion decision index D is used as the basis of detecting the LDoS attacks.Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.展开更多
As the 2nd generation digital terrestrial television broadcasting(DTTB)standard,digital terrestrial/television multimedia broadcasting-advanced(DTMB-A)can provide higher spectrum efficiency and transmission reliabilit...As the 2nd generation digital terrestrial television broadcasting(DTTB)standard,digital terrestrial/television multimedia broadcasting-advanced(DTMB-A)can provide higher spectrum efficiency and transmission reliability by adopting flexible frame structure and advanced forward error correction coding compared with the 1 st generation DTTB systems.In order to increase the flexibility and robustness of the DTTB network,the frequency reuse scheme of factor one(reuse-1)is proposed,where the same RF channel is used by different stations covering the adjacent service areas.However,it demands a very low carrier-tonoise ratio(C/N)threshold below 0 dB at the DTTB physical layer.In this paper,a robust broadcasting technique is proposed based on DTMB-A with newly designed low-rate low density parity check(LDPC)codes.By adopting quasi-cyclic(QC)Raptor-like structure and progressive lifting method,the high performance low-rate LDPC codes are designed supporting multiple code lengths.Both density-evolution analyses and laboratory measurements demonstrate that DTMB-A with low-rate coding can complete the demodulation reliably with the C/N threshold below0 d B,which is one important necessary condition to support frequency reuse-1 scheme.展开更多
针对低采样率下交互式投票地图匹配(interactive-voting based map matching,IVMM)算法的匹配准确率和匹配效率较低的问题,提出一种低采样率条件下的改进交互式投票地图匹配算法。通过建立道路网络的R树索引,提升空间中数据的搜索效率,...针对低采样率下交互式投票地图匹配(interactive-voting based map matching,IVMM)算法的匹配准确率和匹配效率较低的问题,提出一种低采样率条件下的改进交互式投票地图匹配算法。通过建立道路网络的R树索引,提升空间中数据的搜索效率,优化观测概率和转移概率公式改进时空分析;借助平均速度和采样时间得出估计路径长度,分析候选路径与实际路径的相关性,以降低误匹配,提升匹配的准确率;设定3个约束条件以减少错误候选路段,降低算法的计算量继而缩短匹配用时。仿真实验表明,在3种路况条件下,改进的算法优于4个对比算法,匹配准确率可保持在90.1%以上。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.
文摘The study on hot deformation behavior of 316LN stainless steel at low-rate strain is very seldom at present. By sam-pling from industrial 316LN stainless steel and carrying out a compression heat deformation test by means of a Gleeble-3800 thermal simulation testing machine at a strain rate of 3×10^-3s^-1 and a deformation temperature of 600 - 1 100℃, true stress- strain curves are obtained. By analyzing the true stress-strain curves and microscopic structure of the samples, it can be conclu-ded that 1 000℃ and slightly higher temperature are suitable for low-rate strain processing of 316LN stainless steel.
基金supported in part by the National Natural Science Foundation under grant No.U1533107the Major Program of Natural Science Foundation of Tianjin under grant No.17JCZDJC30900+1 种基金the Fundamental Research Funds for the Central Universities of CAUC under grant No.3122016D003the graduate program of curriculum development project of Civil Aviation University of China(2050070515)
文摘In frequency domain,the power spectrum of Low-rate denial of service(LDoS) attacks is totally spread into the spectrum of normal traffic.It is a challenging task to detect and filter LDoS attack flows from the normal traffic.Based on the analysis of LDoS attack flows and legitimate TCP traffic in time and frequency domains,the periodicity of the TCP traffic and LDoS attack flows is explored to facilitate the research of network traffic processing.Hence,an approach of LDoS attack flow filtering based on frequency spectrum analysis is proposed.In this approach,the TCP traffic and LDoS attack flows are transformed from the time domain into the frequency domain.Then the round-trip time(RTT) is estimated by using frequency domain search method.Analysis of amplitude spectrum shows that TCP traffic energy is mainly concentrated on the points of n/RTT.Therefore,a comb filter using infinite impulse response(IIR) filter is designed to filter out the LDoS attack flows in frequency domain,while most legitimate TCP traffic energy at the points of n/RTT are pass through.Experimental results show that the maximum pass rate for legitimate TCP traffic reaches 92.55%,while the maximum filtration rate of LDoS attack flows reaches 81.36%.The proposed approach can effectively filter the LDoS attack flows while less impact on the legitimate TCP traffic.
基金the National Natural Science Foundation of China-Civil Aviation joint fund(U1933108)the Fundamental Research Funds for the Central Universities of China(3122019051).
文摘As a new type of Denial of Service(DoS)attacks,the Low-rate Denial of Service(LDoS)attacks make the traditional method of detecting Distributed Denial of Service Attack(DDoS)attacks useless due to the characteristics of a low average rate and concealment.With features extracted from the network traffic,a new detection approach based on multi-feature fusion is proposed to solve the problem in this paper.An attack feature set containing the Acknowledge character(ACK)sequence number,the packet size,and the queue length is used to classify normal and LDoS attack traffics.Each feature is digitalized and preprocessed to fit the input of the K-Nearest Neighbor(KNN)classifier separately,and to obtain the decision contour matrix.Then a posteriori probability in the matrix is fused,and the fusion decision index D is used as the basis of detecting the LDoS attacks.Experiments proved that the detection rate of the multi-feature fusion algorithm is higher than those of the single-based detection method and other algorithms.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant 61931015the Peng Cheng Laboratory under Grant PCL2021A10+1 种基金the Science,Technology and Innovation Commission of Shenzhen Municipality(No.JSGG20201103095805015)sponsored by Tsinghua University-Yunnan Mobile Digital TV Company Ltd.,Joint Research Center(JCICBN)。
文摘As the 2nd generation digital terrestrial television broadcasting(DTTB)standard,digital terrestrial/television multimedia broadcasting-advanced(DTMB-A)can provide higher spectrum efficiency and transmission reliability by adopting flexible frame structure and advanced forward error correction coding compared with the 1 st generation DTTB systems.In order to increase the flexibility and robustness of the DTTB network,the frequency reuse scheme of factor one(reuse-1)is proposed,where the same RF channel is used by different stations covering the adjacent service areas.However,it demands a very low carrier-tonoise ratio(C/N)threshold below 0 dB at the DTTB physical layer.In this paper,a robust broadcasting technique is proposed based on DTMB-A with newly designed low-rate low density parity check(LDPC)codes.By adopting quasi-cyclic(QC)Raptor-like structure and progressive lifting method,the high performance low-rate LDPC codes are designed supporting multiple code lengths.Both density-evolution analyses and laboratory measurements demonstrate that DTMB-A with low-rate coding can complete the demodulation reliably with the C/N threshold below0 d B,which is one important necessary condition to support frequency reuse-1 scheme.
文摘针对低采样率下交互式投票地图匹配(interactive-voting based map matching,IVMM)算法的匹配准确率和匹配效率较低的问题,提出一种低采样率条件下的改进交互式投票地图匹配算法。通过建立道路网络的R树索引,提升空间中数据的搜索效率,优化观测概率和转移概率公式改进时空分析;借助平均速度和采样时间得出估计路径长度,分析候选路径与实际路径的相关性,以降低误匹配,提升匹配的准确率;设定3个约束条件以减少错误候选路段,降低算法的计算量继而缩短匹配用时。仿真实验表明,在3种路况条件下,改进的算法优于4个对比算法,匹配准确率可保持在90.1%以上。
