电力信息系统中基于受限玻尔兹曼机的多源日志综合特征提取方法

Donglan Liu, Hao Yu, Wenting Wang, Haotong Zhang, Xiaohong Zhao, Yang Zhao, Jianfei Chen, Dong Li
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引用次数: 2

摘要

为了充分利用电力信息系统中的异构数据源挖掘电网中的安全威胁,提出了一种基于受限玻尔兹曼机(RBM)的多源日志综合特征提取方法。首先,利用受限玻尔兹曼机神经网络对各种日志信息进行归一化编码;然后,采用对比散度快速学习方法优化网络权值,采用随机梯度上升法最大化对数似然函数,对RBM模型进行训练和学习。通过对规范化的编码日志信息进行处理,实现数据降维。同时,获得了综合特征,有效解决了测井数据异质性带来的问题。在电力信息系统中搭建实验环境,对安全日志进行综合特征提取和算法验证。实验结果表明,该方法可应用于各类安全分析,如聚类分析、异常检测等,可有效提高电力信息系统安全态势预测的速度和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-source Log Comprehensive Feature Extraction Method Based on Restricted Boltzmann Machine in Power Information System
In order to excavate security threats in power grid by making full use of heterogeneous data sources in power information system, this paper proposes a multi-source log comprehensive feature extraction method based on restricted boltzmann machine (RBM). Firstly, the restricted boltzmann machine neural network is used to normalize coding all kinds of log information. Then, the contrast divergence fast learning method is used to optimize the network weight, and the stochastic gradient rise method is used to maximize the logarithmic likelihood function for the training and learning of the RBM model. The data dimension reduction is realized by processing the normalized coded log information. At the same time, the comprehensive features are obtained, which can effectively solve the problems caused by the heterogeneity of log data. The experimental environment was set up in the power information system, and the comprehensive feature extraction and algorithm verification of the security log were carried out. Experimental results show that the proposed method can be applied to all kinds of security analysis, such as clustering analysis, anomaly detection, etc., and it can effectively improve the speed and accuracy of power information system security situation prediction.
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