Local ageing defects are the main causes of cross-linked polyethylene(XLPE)power cable faults.This research aims at assessing the severity level of typical cable local ageing defects based on the high-voltage frequenc...Local ageing defects are the main causes of cross-linked polyethylene(XLPE)power cable faults.This research aims at assessing the severity level of typical cable local ageing defects based on the high-voltage frequency domain spectroscopy(FDS)non-linearity.Experimentally,typical XLPE cables ageing defect conditions are simulated,with different ageing types,degrees,and local proportions.Then,the high-voltage FDS detection is conducted and the results have been analysed.It has been found that the high-voltage FDS non-linearity is only related to the detection voltage and ageing degree.Especially,it is more sensitive to changes in the ageing severity.Thus,the local ageing defect severity assessment method based on non-linearity has been established.Additionally,the sensitivity has been compared between two diagnostic evaluation methods:the high-voltage FDS and the widely used very-low frequency method(VLF,0.1 Hz).It can be concluded from the comparison result that the proposed assessment method has higher sensitivity than that of the traditional methods.Finally,the effectiveness of the proposed method for evaluating the on-site long cables has been validated.The result demonstrates that the proposed method can assess on-site long cables and has the potential to be formed as quantitative standards.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2023YFB2406900。
文摘Local ageing defects are the main causes of cross-linked polyethylene(XLPE)power cable faults.This research aims at assessing the severity level of typical cable local ageing defects based on the high-voltage frequency domain spectroscopy(FDS)non-linearity.Experimentally,typical XLPE cables ageing defect conditions are simulated,with different ageing types,degrees,and local proportions.Then,the high-voltage FDS detection is conducted and the results have been analysed.It has been found that the high-voltage FDS non-linearity is only related to the detection voltage and ageing degree.Especially,it is more sensitive to changes in the ageing severity.Thus,the local ageing defect severity assessment method based on non-linearity has been established.Additionally,the sensitivity has been compared between two diagnostic evaluation methods:the high-voltage FDS and the widely used very-low frequency method(VLF,0.1 Hz).It can be concluded from the comparison result that the proposed assessment method has higher sensitivity than that of the traditional methods.Finally,the effectiveness of the proposed method for evaluating the on-site long cables has been validated.The result demonstrates that the proposed method can assess on-site long cables and has the potential to be formed as quantitative standards.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
