Research Of Anomaly Detection Based On Flower Pollination And Cluster Analysis Algorithm

Cluster analysis,an unsupervised machine learning method,has established anomaly detection models from unidentified data sets without a rule base,so it occupies an important position in the anomaly detection field.The fuzzy c-means clustering algorithm(fuzzy C-means,FCM)has achieved more efficient clustering by fuzzy division of the target set,but it is very sensitive to the selection of the initial clustering center.Aiming at the problem of excessive dependence of the initial value of the FCM algorithm,this paper adds an adaptive mechanism and a Cauchy mutation mechanism to the flower pollination algorithm to achieve the optimal selection of the initial clustering center of the FCM.Cluster anomaly detection method based on flower pollination improves detection rate and reduces false alarm rate.In traditional flower pollination algorithm(FPA),the switching probability value is fixed and the individual flower evolution is blind,which easily leads to the problem of low optimization accuracy.This paper adds an adaptive mechanism to the switching probability to dynamically change with the number of iterations The Cauchy mutation mechanism was added to the individual flower before entering an iteration.The improved algorithm has high optimization precision and is more suitable for clustering anomaly detection.A clustering anomaly detection algorithm based on improved flower pollination algorithm is proposed.The improved flower pollination algorithm is applied to FCM,and the optimal solution obtained by the improved flower pollination algorithm is used to perform FCM cluster analysis,thereby solving the problems of FCM sensitivity to initial values and transition dependence on initial values.At the same time,replacing the Euclidean distance in the FCM algorithm with the Mahalanobis distance solves the problem that the FCM algorithm cannot handle high-dimensional data.Experimental results show that the improved algorithm takes into account the efficiency of data detection,and the algorithm improves the detection rate and reduces the false alarm rate in anomaly detection.At the same time,the improved flower pollination algorithm solves the defect of FCM's over-reliance on the initial value,improves the convergence speed of the overall algorithm and reduces the amount of calculation...