DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

Abstract

In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious.