基于随机森林的工控企业网络安全智能故障诊断研究

Research on Intelligent Fault Diagnosis of Industrial Control Enterprise Network Security Based on Random Forest

由于工控企业网络安全具有非线性特点,在网络故障智能诊断中,提出一种精确度加权随机森林算法(AWRF),该算法基于随机森林算法原理,根据预测样本作为其对应的权重,解决在故障诊断中数据不均衡的问题。实证表明:该算法简化随机森林计算的复杂度,加快程序运行,降低故障诊断的错误率。

Due to the nonlinear characteristics of network security in industrial control enterprises, proposes an accuracy weighted random forest algorithm（AWRF） in the intelligent diagnosis of network fault. Based on the principle of random forest algorithm, the algorithm solves the problem of disequilibrium of data in fault diagnosis based on the predicted sample as its corresponding weight. The empirical results show that the algorithm simplifies the complexity of random forest computation, speeds up program operation, and reduces the error rate of fault diagnosis.