动态特征选择算法对恶意行为检测的优化研究

Optimization of dynamic feature selection algorithm for malicious behavior detection

针对互联网中存在的恶意行为,特别是社交网络应用中的在线恶意行为,通常使用基于用户多维特征的聚类分析算法进行检测。提出一种动态特征选择算法(DFSA),使用具有特征加权熵的模糊C均值目标函数,首先为参数构建一个学习模式,自动计算每个特征权重,并剔除权重小于阈值的特征,动态选择重要的特征,迭代地更新隶属函数、簇中心和特征权重直到最优化为止,最后识别出具有高精度的恶意用户行为簇。仿真结果表明,对比SDAFS算法、ELAFC算法和NADMB算法,DFSA算法在Rand指数、Jaccard指数和归一化互信息量3个主要性能指标上均有改善。

For malicious behaviors existing in the Internet,especially online malicious user behavior detection in social network applications,clustering analysis algorithms based on multi-dimensional user characteristics are usually used for detection.This paper proposes a dynamic feature selection algorithm(DFSA),which uses a fuzzy C-means objective function with feature weighted entropy.Firstly,a learning mode is constructed for the parameters,and each feature weight is automatically calculated,and features whose weight is less than the threshold are eliminated.Important feature components are selected dynamically,and the membership function,cluster center and feature weights are updated iteratively until the optimization is achieved.Finally,malicious user behavior clusters with high accuracy is detect-ed.The simulation results show that the proposed algorithm outperforms the SDAFS algorithm,the ELAFC algorithm and the NADMB algorithm in terms of three main performance indicators such as Rand index,Jaccard index and normalized mutual information.