Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

An ultra-sensitive biosensor for circulating microRNA detection with Fe single-atom enhanced cathodic luminol-O_(2) electrochemiluminescence

Circulating microRNAs(miRNAs)play a pivotal role in the occurrence and development of acute myocardial infarction(AMI),and precise detection of them holds significant clinical implications.The development of luminol-based luminophores in the field of electrochemiluminescence(ECL)for miRNA detection has been significant,while their effectiveness is hindered by the instability of co-reactant hydrogen peroxide(H_(2)O_(2)).In this work,an iron single-atom catalyst(Fe-PNC)was employed for catalyzing the luminol-O_(2) ECL system to achieve ultra-sensitive detection of myocardial miRNA.Target miRNA triggers a hybridization chain reaction(HCR),resulting in the generation of a DNA product featuring multiple sticky ends that facilitate the attachment of Fe-PNC probes to the electrode surface.The Fe-PNC catalyst exhibits high promise and efficiency for the oxygen reduction reaction(ORR)in electrochemical energy conversion systems.The resulting ECL biosensor allowed ultrasensitive detection of myocardial miRNA with a low detection limit of 0.42 fM and a wide linear range from 1 fM to 1.0 nM.Additionally,it demonstrates exceptional performance when evaluated using serum samples collected from patients with AMI.This work expands the application of single-atom catalysis in ECL sensing and introduces novel perspectives for utilizing ECL in disease diagnosis.