多模态特征融合的网络安全态势评估

针对网络安全监测设备信息来源单一以及预警质量较低等问题,文中提出了融合多种数据来源的网络安全态势评估方法。通过引入Endsley模型和Agent理论,构建了网络安全态势的NSSA框架。利用径向基神经网络的思想,通过消除多余噪声与无关信号实现多源异构数据的融合,从而提出具有多模态特征融合的网络安全态势评估方法。MATLAB仿真结果表明,与传统的BP神经网络相比,文中提出的网络安全态势评估方法具有更好的学习能力和泛化能力。

Network Security Situation Assessment Based on Multimodal Feature Fusion

In view of the problems of single information source and low early warning quality of network security monitoring equipment, a network security situation assessment method integrating multiple data sources is proposed in this study. The NSSA framework of network security situation is constructed by introducing Endsley model and agent theory. By using the idea of radial basis function neural network, the fusion of multi-source heterogeneous data is realized through eliminating redundant noise and irrelevant signals. Therefore, a network security situation assessment method with multi-modal feature fusion is proposed. The simulation results of MATLAB software show that compared with the traditional BP and RBF neural networks, the proposed network security situation assessment method has better learning ability and generalization ability.