基于极限学习机与改进K-means算法的入侵检测方法

入侵检测系统对于保障网络安全至关重要。针对传统的单一检测算法很难对不同种类的攻击都有很好检测效果的问题，提出一种结合极限学习机与改进K means算法的入侵检测方法。基于算法级联的方式，利用新型线性修正单元（PReLU）激活函数对极限学习机算法进行优化，采用设置距离阈值的方式，实现K means算法自动选择初始聚类中心与聚类簇数目的双重优化，设计了一种混合式入侵检测方法。采用NSL KDD数据集对所提出的入侵检测方法进行仿真实验，实验结果表明，与传统的BP神经网络、支持向量机与极限学习机算法相比，该方法有效地提高了检测效果，同时降低了误报率。

An intrusion detection method based on extreme learning machine and modified K-means

Abstract:Intrusion detection systems are essential to protect the network security. However, it is hard for a traditional single algorithm to attain satisfied detection results for different attack classes. To solve this problem, this paper proposes an intrusion detection method based on Extreme Learning Machine (ELM) and modified K-means. ELM algorithm is optimized by Parametric Rectified Linear Unit (PReLU) activation function. The modified K-means algorithm can automatically select the initial centroids of clusters and the number of clusters by setting the distance threshold. Based on cascade algorithms, a hybrid intrusion detection method is designed based on improved ELM and modified K-means. The experimental results on NSL-KDD dataset show that, compared with other traditional algorithms such as BP neural network, Support Vector Machine (SVM) and ELM, the proposed method improves the detection results and reduces the false alarm rate.