无线网络终端设备安全脆弱点识别仿真

Wireless Network Terminal Equipment Security Vulnerability Identification Simulation

对无线网络的终端设备安全的脆弱点进行识别,能有效提高网络异常检测的准确率,提升无线网络运行的安全性能,延长网络运行的周期。利用当前方法对无线网络终端脆弱点进行识别时,终端数据比较杂乱,对安全脆弱点进行识别时,存在识别的准确率较差的问题。提出基于混沌RBF神经网络的网络终端安全脆弱点检测的方法。对网络终端信号构建信号模型,对网络终端信号进行抗干扰的滤波进行处理,利用混沌RBF神经网络对网络终端脆弱点进行检测与识别,对混沌时间的序列进行空间的重构得出空间的向量,作为RBF神经网络数据的输入,经过RBF神经网络对网络终端数据拟合函数进行构建,在拟合函数上进行预测,将预测值和真实值存在的偏差进行对比,由此判断检测的信号是否为异常信号,完成对无线网络终端设备安全脆弱点的识别。实验的结果可表明,利用所提方法,能有效对安全脆弱点进行识别,提高了安全脆弱点识别的准确率。

A method to detect security vulnerabilities of network terminal based on chaotic RBF neural network is presented. Firstly, this method built the model for network terminal signal, and the anti - interference filtration was carried out for network terminal signal. Then, chaotic RBF neural network was used to detect and recognize network terminal vulnerabilities. The space reconstruction was carried out for chaotic time series to get space vectors, which were taken as the input of RBF neural network. Through RBF neural network, fitting function of network terminal data was constructed. By comparing the deviation of predicted value and real value, we determined whether the detected signal was an abnormal signal. Thus, we completed the recognition of security vulnerabilities in wireless network terminal equipment. From simulation result, the proposed method can be used to identify vulnerabilities, which improves the accuracy of vulnerability identification.