关键词: 蝙蝠算法, 智能蝙蝠算法, K-means, 异常数据检测, 聚类准确率

Abstract: 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.

Key words: bat algorithm, intelligent bat algorithm, K-means, abnormal data detection, clustering accuracy