一种公共网络攻击数据挖掘智能算法研究

Research on an intelligent algorithm for public network attack data mining

摘要:

公共网络的开放性和自组织特性导致网络容易受到病毒干扰和入侵攻击,对攻击数据的准确高效挖掘能确保网络安全。传统方法采用时频指向性波束特征聚类方法实现攻击数据挖掘,在信噪比较低时攻击数据准确挖掘概率较低。提出一种基于自适应滤波检测和时频特征提取的公共网络攻击数据挖掘智能算法。首先进行公共网络攻击数据的信号拟合和时间序列分析,对含噪的攻击数据拟合信号进行自适应滤波检测,提高信号纯度,对滤波输出数据进行时频特征提取,实现攻击数据的准确挖掘。仿真结果表明,采用该算法进行网络攻击数据挖掘,对攻击数据特征的准确检测性能较高,对干扰的抑制性能较强,能有效实现网络安全防御。

Abstract:

The open nature of public network and the characteristics of self - organization cause the network to be vulnerable to virus interference and intrusion attacks. The traditional method uses the time-frequency directional beam characteristic clustering method to realize the attack data mining, and the low probability of attack data mining is low when the SNR is low. A common network attack data mining algorithm based on adaptive filtering detection and time frequency feature extraction is proposed. First public network attack data signal fitting and time series analysis, detection adaptive filtering noise attack data to fit the signal and improve the purity of the signal, the filter output data time-frequency feature extraction, to achieve accurate attack data mining. The simulation results show that the proposed algorithm is used for data mining in network attack, which has high performance on the feature of attack data, and has a strong restraining performance against interference.