Hybrid Gaussian Network Intrusion Detection Method Based on CGAN and E-GraphSAGE

The rapid development of the Internet of Things(IoT)and modern information technology has led to the emergence of new types of cyber-attacks.It poses a great potential danger to network security.Consequently,protecting against network attacks has become a pressing issue that requires urgent attention.It is crucial to find practical solutions to combat such malicious behavior.A network intrusion detection(NID)method,known as GMCE-GraphSAGE,was proposed to meet the detection demands of the current intricate network environment.Traffic data is mapped into gaussian distribution,which helps to ensure that subsequent models can effectively learn the features of traffic samples.The conditional generative adversarial network(CGAN)can generate attack samples based on specified labels to create balanced traffic datasets.In addition,we constructed a communication interaction graph based on the connection patterns of traffic nodes.The E-GraphSAGE is designed to capture both the topology and edge features of the traffic graph.From it,global behavioral information is combined with traffic features,providing a solid foundation for classifying and detecting.Experiments on the UNSW-NB15 dataset demonstrate the great detection advantage of the proposed method.Its binary and multi-classification F1-score can achieve 99.36%and 89.29%,respectively.The GMCE-GraphSAGE effectively improves the detection rate of minority class samples in the NID task.

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

用于PPG信号测试的大面积柔性胶体量子点红外光电二极管

以铟镓砷、碲镉汞等外延生长的单晶材料作为吸光层的光电二极管是红外探测技术的主流方案.然而,它们的刚性结构、极高成本以及有限尺寸使其无法满足诸如人体内外环境监测,便携式医疗等智能穿戴应用的需求.基于低温液相工艺的胶体量子点(CQDs)红外光电二极管是实现大面积柔性红外光电探测器极具竞争力的候选方案,但由于其发展年限较短,仍存在暗电流密度大、均匀性差以及工作稳定性差等问题.本文通过在CQDs墨水中引入聚酰亚胺来制备CQDs光电二极管.这种聚合物配体既实现了对CQDs表面缺陷态的钝化,又能够保持胶体墨水的单分散性和持久性.这两点使得光电探测器的吸光层形貌得以改善,同时暗电流密度显著降低,极大地优化了CQDs光电二极管的均匀性和工作稳定性.采用该方案制备的大面积柔性CQDs光电二极管能够实现高于10^(13)Jones的比探测率,可在环境光下实现可穿戴式光电体积描记(PPG)心率信号监测,极大地降低了成本和功耗.

Heterogeneous system synthesis of high quality PbS quantum dots for efficient infrared solar cells

As promising optoelectronic materials,lead sulfide quantum dots(PbS QDs)have attracted great attention.However,their applications are substantially limited by the QD quality and/or complicated synthesis.Herein,a facile new synthesis is developed for highly monodisperse and halide passivated PbS QDs.The new synthesis is based on a heterogeneous system containing a PbCl_(2)-Pb(OA)_(2)solid-liquid precursor solution.The solid PbCl_(2)inhibits the diffusion of monomers and maintains a high oversaturation condition for the growth of PbS QDs,resulting in high monodispersities.In addition,the PbCl_(2)gives rise to halide passivation on the PbS QDs,showing excellent stability in air.The high monodispersity and good passivation endow these PbS QDs with outstanding optoelectronic properties,demonstrated by a 9.43%power conversion efficiency of PbS QD solar cells with a bandgap of~0.95 eV(1,300 nm).We believe that this heterogeneous strategy opens up a new avenue optimizing for the synthesis and applications of QDs.

Au/Ag_(2)S dimeric nanostructures for highly specific plasmonic sensing of mercury(Ⅱ)

Although many plasmonic nanosenosrs have been established for the detection of mercury(Ⅱ)(Hg^(2+)),few of them is feasible for analyzing natural samples with very complex matrices because of insufficient method selectivity.To address this challenge,we propose an epitaxial and lattice-mismatch approach to the synthesis of a unique Au/Ag_(2)S dimeric nanostructure,which consists of an Au segment with excellent plasmonic characteristics,and a highly stable Ag_(2)S portion with minimum solubility product (K_(sp)(Ag_(2)S)=6.3×10^(-50)).The detection relies on the chemical conversion of Ag_(2)S to HgS when reacting with Hg^(2+),resulting in a red shift in the absorption band of the connecting Au NPs.The concurrent color changes of the solution from gray purple to dark green and finally to navy correlate well with Hg^(2+)concentration,thus enables UV-vis quantitation and a naked-eye readout of the Hg^(2+)concentration.This method exhibits superior selectivity towards Hg^(2+) over other interfering ions tested because Hg^(2+) is the only ion that can react with Ag_(2)S to form HgS with even smaller solubility product (K_(sp)(HgS)=4×10^(-53)).The detection limit of this method is 1.21μmol/L,calculated by the signal-to-noise of 3.The practicability of the method was verified by analyzing the Hg^(2+)in sewage water samples without sample pretreatment with satisfactory recoveries (93.1%-102.8%) and relative standard deviations (1.38%-2.89%).We believe this method holds great potential for on-the-spot detection of Hg^(2+) in environmental water samples with complex matrices.

Highly sensitive colorimetric detection of NH3 based on Au@Ag@AgCl core-shell nanoparticles

Abstract:As an important component of the atmosphere,ammonia(NH_(3))plays a very important role in maintaining the balance of environment.However,it is also one of the most toxic gases that can cause damage to the human respiratory system and mucous membranes even at low concentrations.As such,development of highly sensitive and selective NH_(3)sensors is of high significance for environmental monitoring and health maintenance.Herein,we have synthesized Au@Ag@Ag Cl core-shell nanoparticles(NPs)by oxidative etching and precipitating Au@Ag core-shell NPs using FeCl3 and further used them as optical probes for the colorimetric detection of NH_(3).The sensing mechanism is based on the fact that the etching of NH_(3)on AgCl and Ag shell leads to the variations of ingredients and core-to-shell ratio of the Au@Ag@AgCl NPs,thereby inducing noticeable spectral and color changes.By replacing the outmost layer of Ag with AgCl,not only is the stability of the sensor against oxygen significantly enhanced,but also is the sensitivity of the method improved.The method exhibits good linear relationship for the detection of NH_(3)from 0 to 5000 mmol/L with the limit of detection of 6.4 mmol/L.This method was successfully applied to the detection of simulated air polluted by NH_(3),indicating its practical applicability for environmental monitoring.This method shows great potential for on-site NH_(3)detection particularly in remote area,where a simple,fast,low-cost,and easy-to-handle method is highly desirable.