As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved general...As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.展开更多
Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.Ho...Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.However,effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals.Furthermore,inefficient feature extraction results in substantial time wastage,making it hard to apply in realtime monitoring.A novel feature extraction method for diagnosing bearing faults using multiscale improved envelope spectrum entropy(MIESE)is proposed in this work.First,bearing vibration signals are analyzed across multiple scales,and improved envelope spectrum entropy(IESE)is extracted fromthese signals at each scale to form an original feature set.Subsequently,joint approximate diagonalization eigenmatrices(JADE)is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set.Finally,the newly generated feature set is input into support vectormachines(SVMs)to effectively diagnose bearing health status.Two cases studies are employed to demonstrate the reliability of the proposed method.The results illustrate that the proposed method can improve the stability of extracted features and increase the computational efficiency.展开更多
A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monito...A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.展开更多
According to the subjectivity and fuzziness of analysis on failure mode severity about spindle system of machining center,an analysis model of the failure mode severity of such a system is proposed based on the new fa...According to the subjectivity and fuzziness of analysis on failure mode severity about spindle system of machining center,an analysis model of the failure mode severity of such a system is proposed based on the new fault severity index system, improved analytic hierarchy process( IAHP) and entropy-based fuzzy comprehensive evaluation. IAHP and entropy methods are adopted to determine the comprehensive failure severity index weight. The evaluation result is obtained after the factor set,comment set,weight set,and other parameters are determined,and then the level of risk degree and numerical value order of every spindle system failure mode is given. By taking an example,we verify that the proposed method can quantify the qualitative problem comprehensively,obtain more accurate analysis results,and provide the theoretical reference for mechanization and sequencing of failure mode effect analysis in reliability analysis. The calculation results can also serve as the basis of failure mode,effects,and criticality analysis in the subsequent step.展开更多
While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical...While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical data.Because the primary means of operation for these Internet of Things(IoT)devices is constant data transmission,they are vulnerable to cyberthreats including Distributed Denial-of-Service(DDoS)assaults and data injection.This study suggests an AI-based Healthcare Cybersecurity System(AI-HCsS)that integrates blockchain tech-nology to mitigate these vulnerabilities and provide strong,real-time patient data and healthcare system pro-tection.A new architecture is shown to identify and counteract DDoS attacks on the cloud infrastructure,and blockchain is used for safe and unchangeable data storage.The system extracts statistical,raw,and enhanced entropy-based features after performing improved min-max normalization for data pre-processing.Then,for precise DDoS attack detection,a modified Parallel Convolutional Neural Network(PCNN)is used.The model's output is interpreted using the SHapley Additive exPlanations(SHAP)approach,which identifies important characteristics that affect detection performance in order to improve transparency and aid clinical decision-making.According to experimental results,the modified PCNN outperforms traditional methods with a high detection accuracy of 91.1%.In addition to bolstering the cybersecurity of healthcare IoT ecosystems,this in-tegrated solution guarantees the real-time defense of clinical systems and patient data against changing cyberthreats.展开更多
基金This work is supported by the National Natural Science Foundation of China(Grant Nos.U22B2005,62072109)the Natural Science Foundation of Fujian Province(Grant No.2021J01625)the Major Science and Technology Project of Fuzhou(Grant No.2023-ZD-003).
文摘As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.
基金supported in part by the Key Basic Research Project MKF20210008.
文摘Bearing fault diagnosis is vital to safeguard the heath of rotating machinery.It can help to avoid economic losses and safe accidents in time.Effective feature extraction is the premise of diagnosing bearing faults.However,effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals.Furthermore,inefficient feature extraction results in substantial time wastage,making it hard to apply in realtime monitoring.A novel feature extraction method for diagnosing bearing faults using multiscale improved envelope spectrum entropy(MIESE)is proposed in this work.First,bearing vibration signals are analyzed across multiple scales,and improved envelope spectrum entropy(IESE)is extracted fromthese signals at each scale to form an original feature set.Subsequently,joint approximate diagonalization eigenmatrices(JADE)is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set.Finally,the newly generated feature set is input into support vectormachines(SVMs)to effectively diagnose bearing health status.Two cases studies are employed to demonstrate the reliability of the proposed method.The results illustrate that the proposed method can improve the stability of extracted features and increase the computational efficiency.
基金funded by National Natural Science Foundation of China(Grant No. 51805146)the Fundamental Research Funds for the Central Universities (Grant No. B200202221)+1 种基金Jiangsu Key R&D Program (Grant Nos. BE2018004-1, BE2018004)College Students’ Innovative Entrepreneurial Training Plan Program (Grant No. 2020102941513)。
文摘A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51275205)
文摘According to the subjectivity and fuzziness of analysis on failure mode severity about spindle system of machining center,an analysis model of the failure mode severity of such a system is proposed based on the new fault severity index system, improved analytic hierarchy process( IAHP) and entropy-based fuzzy comprehensive evaluation. IAHP and entropy methods are adopted to determine the comprehensive failure severity index weight. The evaluation result is obtained after the factor set,comment set,weight set,and other parameters are determined,and then the level of risk degree and numerical value order of every spindle system failure mode is given. By taking an example,we verify that the proposed method can quantify the qualitative problem comprehensively,obtain more accurate analysis results,and provide the theoretical reference for mechanization and sequencing of failure mode effect analysis in reliability analysis. The calculation results can also serve as the basis of failure mode,effects,and criticality analysis in the subsequent step.
文摘While smart wearables and remote devices have improved the speed of diagnosis and treatment,they have also created significant cybersecurity risks,especially with regard to the confidentiality and integrity of medical data.Because the primary means of operation for these Internet of Things(IoT)devices is constant data transmission,they are vulnerable to cyberthreats including Distributed Denial-of-Service(DDoS)assaults and data injection.This study suggests an AI-based Healthcare Cybersecurity System(AI-HCsS)that integrates blockchain tech-nology to mitigate these vulnerabilities and provide strong,real-time patient data and healthcare system pro-tection.A new architecture is shown to identify and counteract DDoS attacks on the cloud infrastructure,and blockchain is used for safe and unchangeable data storage.The system extracts statistical,raw,and enhanced entropy-based features after performing improved min-max normalization for data pre-processing.Then,for precise DDoS attack detection,a modified Parallel Convolutional Neural Network(PCNN)is used.The model's output is interpreted using the SHapley Additive exPlanations(SHAP)approach,which identifies important characteristics that affect detection performance in order to improve transparency and aid clinical decision-making.According to experimental results,the modified PCNN outperforms traditional methods with a high detection accuracy of 91.1%.In addition to bolstering the cybersecurity of healthcare IoT ecosystems,this in-tegrated solution guarantees the real-time defense of clinical systems and patient data against changing cyberthreats.
