基于Spark框架和PSO优化算法的电力通信网络安全态势预测

Prediction about Network Security Situation of Electric Power Telecommunication Based on Spark Framework and PSO Algorithm

随着电力通信网络规模的不断扩大,电力通信网络不间断地产生海量通信数据。同时,对通信网络的攻击手段也在不断进化,给电力通信网络的安全造成极大威胁。针对以上问题,结合Spark大数据计算框架和PSO优化神经网络算法的优点,提出基于Spark内存计算框架的并行PSO优化神经网络算法对电力通信网络的安全态势进行预测。本研究首先引入Spark计算框架,Spark框架具有内存计算以及准实时处理的特点,符合电力通信大数据处理的要求。然后提出PSO优化算法对神经网络的权值进行修正,以增加神经网络的学习效率和准确性。之后结合RDD的并行特点,提出了一种并行PSO优化神经网络算法。最后通过实验比较可以看出,基于Spark框架的PSO优化神经网络算法的准确度高,且相较于传统基于Hadoop的预测方法在处理速度上有显著提高。

With the expansion of the scale of electric power communication network,the electric power communication network continuously produce huge amounts of data communication.At the same time,the communication network attack means is in constant evolution,which brings threats for the safety of the electric power communication network.To solve above problems,combining with the Spark big data computing framework and the advantages of PSO,the Spark memory computing framework of parallel PSO optimization neural network algorithm is put forward to predict the security situation of electric power communication network.This study first introduced the Spark computing framework,the Spark frame has the characteristics of memory computing and quasi real-time processing,accord with the requirement of electric power communication big data processing.Then PSO optimization algorithm was proposed to modify the weights of neural network,in order to increase the study efficiency and accuracy of neural network.Then with the combination of RDD parallel characteristic,this paper proposed a parallel PSO optimization neural network algorithm.Through experiment and comparison,you can see that Spark framework based PSO optimization neural network algorithm has high accuracy,and compared with prediction method based on Hadoop,its processing speed has improved significantly.