无线网络恶意入侵数据自动识别仿真研究

Simulation Research on Automatic Identification of Malicious Intrusion Data in Wireless Networks

对无线网络入侵数据识别问题的研究,能够有效提高无线网络安全性。对恶意入侵数据的识别,需要计算恶意入侵数据阈值,对其二维信息熵进行过滤,完成恶意入侵数据的识别。传统方法先对入侵数据进行加权处理,剔除差异特征识别误差,但忽略了过滤数据得到二维信息熵过程,导致识别精度低。提出基于二维信息熵的无线网络恶意入侵数据自动识别方法。识别不同类型恶意入侵数据,给出差异特征间最大识别结构,引入映射差异特征空间特征量,对各差异特征二维信息熵进行最大寻优,确定差异特征识别阈值,并对冗余差异特征进行过滤,完成无线网络恶意入侵数据的识别。实验结果表明,上述方法对恶意入侵数据识别精度较高,有效保证了无线网络运行安全性。

The research on recognition of intrusion data in wireless network can effectively improve the security of wireless networks. In the traditional method, the intrusion data is weighted to eliminate the recognition error of difference characteristics, but the process that filtered data obtains the two - dimensional information entropy is ignored. An automatic recognition method for malicious intrusion data in wireless networks based on two - dimensional information entropy was proposed. In this method, different types of malicious intrusion data were identified, and the maximum recognition structure between difference characteristics was given. Then, the characteristic quantity of mapping differential feature space was introduced for the maximum optimization of two - dimensional information entropy of different characteristics. In order to determine the threshold of difference feature recognition, the redundant difference feature was filtered. Thus, we completed recognition of malicious intrusion data in wireless network. Simulation results show that this method has high recognition accuracy for malicious intrusion data, which effectively ensures the security of operation for wireless network.