基于非线性预处理及逻辑回归的异常检测算法

An algorithm for anomaly detection based on nonlinear preprocessing and logistic regression

随着信息技术的快速发展,网络安全问题日益突出,呈现出异常流量类型多样化、形式复杂化且数据爆发式增长的特性。针对这一问题,提出了一种对数据进行非线性预处理的方式,将原始复杂的数据集转换为可以直接输入学习模型的形式,再利用逻辑回归对高维数据也能表现出很好的处理效果的特性,对数据集进行异常检测。通过理论分析和实验仿真,证明了用该方法进行异常检测具有较好的效果,拥有研究和应用的实际意义。

With the rapid development of information technology,the problem of network security is becoming more and more serious,which presents the characteristics of diversity,complexity and explosive growth of abnormal data flow. In order to solve this problem,this paper proposes a nonlinear pretreatment methods for data set,which can convert the complex and intractable data set into a form that can be directly input into the learning model,and logistic regression can also show good processing effect on high-dimensional data in detecting abnormal data.Through theoretical analysis and experimental simulation,it is proved that this method has a good effect,and has the practical significance of research and application.