The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.

中文翻译：

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大规模物联网网络安全状况预测

物联网（IoT）是近年来在各个领域迅速发展的一项新技术。随着物联网技术在生产和生活中的不断应用，物联网的网络安全问题日益突出。为了应对物联网技术发展带来的挑战，本文着重于网络安全态势感知。网络安全状况感知是物联网网络安全的基础。本质上，网络安全状况预测是一种时间序列预测问题。因此有必要构造一个适合时间序列数据的修正函数来修正传统支持向量机（SVM）的核函数。提出了一种改进的物联网网络安全态势感知模型。获得了序列核支持向量机，并采用粒子群算法（PSO）对相关参数进行了优化。通过收集大学校园物联网的边界数据证明了该方法是可行的。最后，与PSO-SVM进行了比较，证明了该方法在提高物联网网络安全态势预测准确性方面的有效性。实验结果表明，在网络安全状况预测中，PSO-时间序列核支持向量机优于PSO-Gauss核支持向量机。Hadoop平台的应用还提高了数据处理效率。通过收集大学校园物联网的边界数据证明了该方法是可行的。最后，与PSO-SVM进行了比较，证明了该方法在提高物联网网络安全态势预测准确性方面的有效性。实验结果表明，在网络安全状况预测中，PSO-时间序列核支持向量机优于PSO-Gauss核支持向量机。Hadoop平台的应用还提高了数据处理效率。通过收集大学校园物联网的边界数据证明了该方法是可行的。最后，与PSO-SVM进行了比较，证明了该方法在提高物联网网络安全态势预测准确性方面的有效性。实验结果表明，在网络安全状况预测中，PSO-时间序列核支持向量机优于PSO-Gauss核支持向量机。Hadoop平台的应用还提高了数据处理效率。实验结果表明，在网络安全状况预测中，PSO-时间序列核支持向量机优于PSO-Gauss核支持向量机。Hadoop平台的应用还提高了数据处理效率。实验结果表明，在网络安全状况预测中，PSO-时间序列核支持向量机优于PSO-Gauss核支持向量机。Hadoop平台的应用还提高了数据处理效率。