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.

Gonorrhea cluster detection in Manitoba,Canada:Spatial,temporal,and spatio-temporal analysis

In Canada,Gonorrhea infection ranks as the second most prevalent sexually transmitted infection.In 2018,Manitoba reported an incidence rate three times greater than the national average.This study aims to investigate the spatial,temporal,and spatio-temporal patterns of Gonorrhea infection in Manitoba,using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016.Age and sex patterns indicate that females are affected by infections at younger ages compared to males.Moreover,there is an increase in repeated infections in 2016,accounting for 16%of the total infections.Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation,demonstrating a clustered distribution of the infection.Northern districts of Manitoba and central Winnipeg were identified as significant clusters.Temporal analysis shows seasonal patterns,with higher infections in late summer and fall.Additionally,spatio-temporal analysis reveals clusters during high-risk periods,with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014,and a secondary cluster in central Winnipeg from June 2004 to November 2012.This study identifies that Gonorrhea infection transmission in Manitoba has temporal,spatial,and spatio-temporal variations.The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention,control measures,and resource allocation.

A novel AE source localization method using clustering detection to eliminate abnormal arrivals

Due to the significant effect of abnormal arrivals on localization accuracy,a novel acoustic emission(AE)source localization method using clustering detection to eliminate abnormal arrivals is proposed in the paper.Firstly,iterative weight estimation is utilized to obtain accurate equation residuals.Secondly,according to the distribution of equation residuals,clustering detection is used to identify and exclude abnormal arrivals.Thirdly,the AE source coordinate is recalculated with remaining normal arrivals.Experimental results of pencil-lead breaks indicate that the proposed method can achieve a better localization result with and without abnormal arrivals.The results of simulation tests further demonstrate that the proposed method possesses higher localization accuracy and robustness under different anomaly ratios and scales;even with abnormal arrivals as high as 30%,the proposed localization method still holds a correct detection rate of 91.85%.

Intrusion Detection Algorithm Based on Density,Cluster Centers,and Nearest Neighbors

Intrusion detection aims to detect intrusion behavior and serves as a complement to firewalls.It can detect attack types of malicious network communications and computer usage that cannot be detected by idiomatic firewalls.Many intrusion detection methods are processed through machine learning.Previous literature has shown that the performance of an intrusion detection method based on hybrid learning or integration approach is superior to that of single learning technology.However,almost no studies focus on how additional representative and concise features can be extracted to process effective intrusion detection among massive and complicated data.In this paper,a new hybrid learning method is proposed on the basis of features such as density,cluster centers,and nearest neighbors(DCNN).In this algorithm,data is represented by the local density of each sample point and the sum of distances from each sample point to cluster centers and to its nearest neighbor.k-NN classifier is adopted to classify the new feature vectors.Our experiment shows that DCNN,which combines K-means,clustering-based density,and k-NN classifier,is effective in intrusion detection.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

Performance Analysis of DEBT Routing Protocols for Pocket Switch Networks

Pocket Switched Networks(PSN)represent a particular remittent network for direct communication between the handheld mobile devices.Compared to traditional networks,there is no stable topology structure for PSN where the nodes observe the mobility model of human society.It is a kind of Delay Tolerant Networks(DTNs)that gives a description to circulate information among the network nodes by the way of taking the benefit of transferring nodes from one area to another.Considering its inception,there are several schemes for message routing in the infrastructure-less environment in which human mobility is only the best manner to exchange information.For routing messages,PSN uses different techniques such asDistributed Expectation-Based Spatio-Temporal(DEBT)Epidemic(DEBTE),DEBT Cluster(DEBTC),and DEBT Tree(DEBTT).Understanding on how the network environment is affected for these routing strategies are the main motivation of this research.In this paper,we have investigated the impact of network nodes,the message copies per transmission,and the overall carrying out of these routing protocols.ONE simulator was used to analyze those techniques on the basis of delivery,overhead,and latency.The result of this task demonstrates that for a particular simulation setting,DEBTE is the best PSN routing technique among all,against DEBTC and DEBTT.

Edge-Weighted Centroidal Voronoi Tessellations

Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy—a sum of the classic CVT energy and the weighted length of cluster boundaries.To distinguish it with the classic CVTs,we call it an Edge-Weighted CVT(EWCVT).The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries.The EWCVT method is easy in implementation,fast in computation,and natural for any number of clusters.

Specific detection and effective inhibition of a single bacterial species in situ using peptide mineralized Au cluster probes

Increasingly serious microbial infections call for the development of new simpler methods for the precise diagnosis and specific inhibition of such pathogens. In this work, a peptide mineralized Au cluster probe was applied as a new simplified strategy to both recognize and inhibit a single bacteria species of Staphylococcus aureus(S. aureus) simultaneously. The probes are composed of peptides and Au clusters. Moreover, the peptides specifically target S. aureus cells and the Au clusters provide fluorescent imaging and have an antibacterial effect. These new probes enable the simultaneous specific detection and effective destruction S. aureus cells in situ.

Association discovery and outlier detection of air pollution emissions from industrial enterprises driven by big data

Air pollution is a major issue related to national economy and people's livelihood.At present,the researches on air pollution mostly focus on the pollutant emissions in a specific industry or region as a whole,and is a lack of attention to enterprise pollutant emissions from the micro level.Limited by the amount and time granularity of data from enterprises,enterprise pollutant emissions are stll understudied.Driven by big data of air pollution emissions of industrial enterprises monitored in Beijing-Tianjin-Hebei,the data mining of enterprises pollution emissions is carried out in the paper,including the association analysis between different features based on grey association,the association mining between different data based on association rule and the outlier detection based on clustering.The results show that:(1)The industries affecting NOx and SO2 mainly are electric power,heat production and supply industry,metal smelting and processing industries in Beijing-Tianjin-Hebei;(2)These districts nearby Hengshui and Shijiazhuang city in Hebei province form strong association rules;(3)The industrial enterprises in Beijing-Tianjin-Hebei are divided into six clusters,of which three categories belong to outliers with excessive emissions of total vOCs,PM and NH3 respectively.

Simulating orthokinetic heterocoagulation and cluster growth in destabilizing suspensions

Using direct numerical simulation, we investigate the coagulation behavior of non-Brownian colloidal particles as exemplified by Al2O3 particles. This yields the so-called capture efficiency, for which we give an analytical expression, as well as other time-dependent variables such as the cluster growth rate. Instead of neglecting or strongly approximating the hydrodynamic interactions between particles, we include hydrodynamic and non-hydrodynamic interactions in a Stokesian dynamics approach and a comprehensive modeling of the interparticle forces. The resulting parallelized simulation framework enables us to investigate the dynamics of polydisperse particle systems composed of several hundred particles at the same high level of modeling we used for a close investigation of the coagulation behavior of two unequal particles in shear flow. Appropriate cluster detection yields all the information about large destabilizing systems, which is needed for models used in flow-sheet simulations. After non-dimensionalization, the results can be generalized and applied to other systems tending to secondary coagulation

Fast Community Detection Based on Distance Dynamics

The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.