Service-Based Architecture(SBA)of 5G network introduces novel communication technology and advanced features,while simultaneously presenting new security requirements and challenges.Commercial 5G Core(5GC)networks are...Service-Based Architecture(SBA)of 5G network introduces novel communication technology and advanced features,while simultaneously presenting new security requirements and challenges.Commercial 5G Core(5GC)networks are highly secure closed systems with interfaces defined through the 3rd Generation Partnership Project(3GPP)specifications to fulfill communication requirements.However,the 5GC boundary,especially the access domain,faces diverse security threats due to the availability of open-source cellular software suites and SoftwareDefined Radio(SDR)devices.Therefore,we systematically summarize security threats targeting the N2 interfaces at the 5GC boundary,which are categorized as Illegal Registration,Protocol attack,and Signaling Storm.We further construct datasets of attack and normal communication patterns based on a 5G simulated platform.In addition,we propose an anomaly detection method based on Next Generation Application Protocol(NGAP)message sequences,which extracts session temporal features at the granularity of User Equipment(UE).The method combines the Long Short-Term Memory Network(LSTM)and the attention mechanism can effectively mine the dynamic patterns and key anomaly time-steps in the temporal sequence.We conducted anomaly detection baseline algorithm comparison experiments,ablation experiments,and real-world simulation experiments.Experimental evaluations demonstrated that our model can accurately learn the dependencies of uplink and downlink messages for our self-constructed datasets,achieving 99.80%Accuracy and 99.85%F1 Score,which can effectively detect UE anomaly behavior.展开更多
This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC)....This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.展开更多
Analytical difficulties encountered in the determination of ethyl carbamate, a known cancinogen, in a wide variety of wines and spirits have been overcome by spe- cific, sensitive GC/GC and CC/CC/MS methods with a rel...Analytical difficulties encountered in the determination of ethyl carbamate, a known cancinogen, in a wide variety of wines and spirits have been overcome by spe- cific, sensitive GC/GC and CC/CC/MS methods with a relatively shorter extraction procedure. The lowest detection limits were estimated to be 0. 1 and 0. 01μg/L for GC/GC and GC/GC/MS respectively. The RSD of the GC/GC method was 2. 5%.展开更多
Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and ...Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.展开更多
文摘Service-Based Architecture(SBA)of 5G network introduces novel communication technology and advanced features,while simultaneously presenting new security requirements and challenges.Commercial 5G Core(5GC)networks are highly secure closed systems with interfaces defined through the 3rd Generation Partnership Project(3GPP)specifications to fulfill communication requirements.However,the 5GC boundary,especially the access domain,faces diverse security threats due to the availability of open-source cellular software suites and SoftwareDefined Radio(SDR)devices.Therefore,we systematically summarize security threats targeting the N2 interfaces at the 5GC boundary,which are categorized as Illegal Registration,Protocol attack,and Signaling Storm.We further construct datasets of attack and normal communication patterns based on a 5G simulated platform.In addition,we propose an anomaly detection method based on Next Generation Application Protocol(NGAP)message sequences,which extracts session temporal features at the granularity of User Equipment(UE).The method combines the Long Short-Term Memory Network(LSTM)and the attention mechanism can effectively mine the dynamic patterns and key anomaly time-steps in the temporal sequence.We conducted anomaly detection baseline algorithm comparison experiments,ablation experiments,and real-world simulation experiments.Experimental evaluations demonstrated that our model can accurately learn the dependencies of uplink and downlink messages for our self-constructed datasets,achieving 99.80%Accuracy and 99.85%F1 Score,which can effectively detect UE anomaly behavior.
基金supported by Hunan 2011 Collaborative Innovation Center of Chemical Engineering&Technology with Environmental Benignity and Effective Resource Utilization,Hunan Province Natural Science Fund,China(Grant Nos.:2020JJ4569,2023JJ60378)Hunan Province College Students'Innovation and Entrepreneurship Training Program,China(Grant Nos.:S202110530044,S202210530048).
文摘This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.
文摘Analytical difficulties encountered in the determination of ethyl carbamate, a known cancinogen, in a wide variety of wines and spirits have been overcome by spe- cific, sensitive GC/GC and CC/CC/MS methods with a relatively shorter extraction procedure. The lowest detection limits were estimated to be 0. 1 and 0. 01μg/L for GC/GC and GC/GC/MS respectively. The RSD of the GC/GC method was 2. 5%.
基金This work was supported by the Project of National Science ＆ Technology Pillar Plan （2012BAK08B03 ）；the National Major Project for Agro - product Quality ＆ Safety Risk Assessment （ GJFP2016006）；the National Natural Science Foundation of China （21205118 ）；the earmarked fund for China Agriculture research system （ CARS - 13 ）.
文摘Developing a method of adulteration detection is critical for protecting customers＂ rights which is a particular concern in food quality. In this study, fatty acid profiles of castor oils were estab-lished by GC and employed to classify 4 types of edible oils and castor oil with multivariate statistical methods. The results indicated that fatty acid profiles of edible oils could be used to classify the 5 kinds of oils. Meanwhile, simulated data test indicated that fatty acid profiles could be used to detect adultera-ted by 5% . Finally, a RF model was built to detect adulteration of edible oils with castor oils by fatty acid composition. The results from cross validation indicated that the oils adulterated by castor oil at low levels （5% 7V/V） could be completely separated from 4 kinds of edible oils. Therefore this model could be used to detect adulteration of 4 kinds of edible oil with castor oils.
