针对电子物联网安全态势感知中面临的静态权重分配难以精确反映网络实际安全态势,导致感知精度受限的问题,提出基于改进贝叶斯网络的电子物联网安全态势感知方法。依据电子物联网的特性和安全需求,挑选并分类关键的态势指标。针对电子物联网安全态势评估的复杂性和多变性,构建了改进的贝叶斯网络模型,通过引入属性加权算法和判断因子,提升模型的分类准确性和稳健性,有效应对不同态势指标的差异性和重要性。在此基础上,逐层融合底层态势指标并量化显著影响因素,最终确定了电子物联网的安全态势等级,实现了对电子物联网安全态势的精准感知与评估。实验结果表明,研究方法的感知态势值与实际态势值高度吻合,能够显著提高电子物联网安全态势感知的精度。

Aiming at the difficulty of static weight allocation in electronic Internet of Things(EIoT) security situation awareness, which is difficult to accurately reflect the actual network security situation, resulting in limited perception accuracy, this paper proposes an improved Bayes network based EIoT security situation awareness method. It selects and categorizes key situational indicators based on the characteristics and security requirements of EIoT. In view of the complexity and variability of the security situation assessment of EIoT, an improved Bayesian network model is constructed, and the attribute weighting algorithm and judgment factor are introduced to improve the classification accuracy and robustness of the model, and effectively cope with the difference and importance of different situation indicators. On this basis, by integrating the underlying situation indicators layer by layer and quantifying significant influencing factors, the security situation level of EIoT is finally determined, and the accurate perception and evaluation of the security situation of EIoT is realized. The experimental results show that the perceived situation value of the research method is highly consistent with the actual situation value, which can significantly improve the accuracy of the security situation awareness of EIoT.