中文摘要：

基于ReliefF的入侵特征选择方法,结合入侵检测数据集类内紧密和类外差距大的特点,通过对入侵特征权重计算的优化,提出一种改进算法：Re-ReliefF算法,解决了网络安全领域数据维度导致处理效率较低的问题.实验结果表明,在安全测试数据集下,改进算法相对传统算法在性能上有一定提高.

英文摘要：

The authors analyzed the intrusion feature selection methods and algorithms based on ReliefF. Combining with the characteristics, in which data points have high similarity in the same class and nonsimilarity in different classes, and optimizing the compute method of intrusion feature weight, we proposed an improved algorithm Re-ReliefF. It resolved the problems about processing efficiency with the data dimensions in network security. Experiments on the network security test datasets showed the effectiveness of the proposed algorithms, and the improved algorithm has advantages on performance compared to the traditional one.