海量网页挂码信息自动采集方法仿真
发布时间：2018-10-30 08:06
【摘要】：为了更好的保障网络信息的安全稳定性,需要进行海量网页挂码信息自动采集方法研究,但是采用当前方法进行网页挂码信息自动采集时,无法构造网页挂码信息的高维特征空间,存在网页挂码信息自动采集精度低的问题。为此,提出一种基于改进神经网络的海量网页挂码信息自动采集方法。上述方法先采用神经网络对海量网页挂码信息样本进行标准化,得到信息特征的模糊隶属度函数,利用梯度优化法进行网络训练,将最小二乘支持向量机参数编码定义为蝙蝠个体,并以海量网页挂码信息自动采集有效性作为参数目标优化函数,通过模拟蝙蝠飞行过程搜索到最小二乘支持向量机最优参数,以此为依据完成对海量网页挂码信息自动采集。仿真证明,所提方法信息采集精度较高,可以为保障网络信息的安全稳定性提供可行的依据。
[Abstract]:In order to better guarantee the security and stability of network information, it is necessary to study the automatic collection method of massive web page hanging code information, but when the current method is used to automatically collect the web page code information, It is impossible to construct the high dimensional feature space of web page code information, which has the problem of low precision of automatic collection of web page code information. In this paper, an improved neural network based method for automatic collection of massive web page coding information is proposed. Firstly, the neural network is used to standardize the information samples of massive web pages, and the fuzzy membership function of the information features is obtained, and the gradient optimization method is used to train the network. The least-squares support vector machine (LS-SVM) parameter coding is defined as bat individual, and the validity of massive webpage information collection is taken as the optimization function of parameter target, and the optimal parameters of LS-SVM are obtained by simulating bat flight process. Based on this, the automatic collection of massive web page code information is completed. Simulation results show that the proposed method has a high accuracy and can provide a feasible basis for ensuring the security and stability of network information.
【作者单位】： 西北师范大学计算机科学与工程学院;
【分类号】：TP393.08
，
本文编号：2299429
[Abstract]:In order to better guarantee the security and stability of network information, it is necessary to study the automatic collection method of massive web page hanging code information, but when the current method is used to automatically collect the web page code information, It is impossible to construct the high dimensional feature space of web page code information, which has the problem of low precision of automatic collection of web page code information. In this paper, an improved neural network based method for automatic collection of massive web page coding information is proposed. Firstly, the neural network is used to standardize the information samples of massive web pages, and the fuzzy membership function of the information features is obtained, and the gradient optimization method is used to train the network. The least-squares support vector machine (LS-SVM) parameter coding is defined as bat individual, and the validity of massive webpage information collection is taken as the optimization function of parameter target, and the optimal parameters of LS-SVM are obtained by simulating bat flight process. Based on this, the automatic collection of massive web page code information is completed. Simulation results show that the proposed method has a high accuracy and can provide a feasible basis for ensuring the security and stability of network information.
【作者单位】： 西北师范大学计算机科学与工程学院;
【分类号】：TP393.08
，
本文编号：2299429
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