A method of classifying network security data based on multi-featured extraction is proposed to address instability of a nonlinear time series in a network security threat. Cybersecurity information is divided in line with the principle of acquiring multiple attributes. On this basis, an adaptive adaptation estimation technology is optimized in analogue. With the proposed method, a cybersecurity information classification system is constructed according to the phase interval reconstruction principle so that a dynamic and autonomous adaptation estimation of the cybersecurity threat can be completed to ensure the feasibility of cybersecurity information classification. The experimental result proves that the cybersecurity information classification technology based on multi-attribute extraction can effectively guide chaos into adjacent orbits and reasonably control the training scale. Moreover, the accuracy of the estimation is guaranteed and the cybersecurity threat is estimated because of its high-speed convergence and strong proximity. Therefore, the proposed classification technology can assist professionals and backstage managers in guaranteeing security by facilitating receipt of information in a timely manner.

中文翻译：

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基于多特征提取的网络安全数据分类方法

针对网络安全威胁中非线性时间序列的不稳定性问题，提出一种基于多特征提取的网络安全数据分类方法。网络安全信息按照多属性获取原则进行划分。在此基础上，模拟优化自适应自适应估计技术。该方法根据相位区间重构原理构建网络安全信息分类系统，完成对网络安全威胁的动态、自主自适应估计，保证网络安全信息分类的可行性。实验结果证明，基于多属性提取的网络安全信息分类技术能够有效引导混沌进入相邻轨道，合理控制训练规模。此外，由于其高速收敛性和强接近性，保证了估计的准确性，并估计了网络安全威胁。因此，所提出的分类技术可以帮助专业人员和后台管理人员通过及时接收信息来保证安全。