基于智能蝙蝠算法的异常数据检测方法

随着大数据应用的普及，网络攻击日益严重并已成为主要的网络安全问题。针对大数据环境下的网络攻击检测问题，设计一种融合聚类和智能蝙蝠算法（DEBA）的网络攻击检测系统。该系统将K-means算法与蝙蝠算法相结合进行数据流分类，实现了对异常数据的高效检测。实验结果显示，该系统的聚类准确率、算法耗时和误报率方面明显优于基于传统蝙蝠算法的K-means算法和单独K-means算法的网络异常数据检测方法。

Abnormal Data Detection Method Based on Intelligent Bat Algorithm

With the popularity of big data applications, network attacks become more serious and become the main network security problems. Aiming at the problem of network attack detection in large data environment, a network attack detection system is designed, which combines clustering with intelligent bat algorithm (DEBA). The system combines K-means algorithm with bat algorithm to classify data stream, and achieves efficient detection of abnormal data. The experimental results show that the clustering accuracy, algorithm time-consuming and false alarm rate of the system are obviously better than the K-means algorithm based on the traditional bat algorithm and the K-means algorithm based on the single network anomaly detection method.