Design of Network Security Situation Awareness and Early Warning System Based on Big Data

The system integration and complexity in BD (big data) environment are very high. There are many uncertainties in NSS (Network Security Situation) evaluation due to the complexity of the system and the insufficient level of human cognition in massive data. The evaluation and prediction of network security SA (situation awareness) can provide basis and guidance for network managers to take measures to deal with network security threats in time and space, and provide reliable guarantee for maintaining the stability of network security. This paper mainly studies the evaluation method of NSS based on improved evidence theory in BD environment. By linearly weighting the objective and subjective local conflicts, the original evidence source is modified, and the comprehensive weight of the evidence source is determined. Finally, the mass function of the evidence is modified. Finally, a distributed security early warning system is constructed. The experimental results show that the MSE of NSS evaluation method based on the improved evidence theory in this paper is 0.002, which is 0.011 lower than that of traditional D-S and 0.031 lower than that of BP neural network. The experimental results show that this method can reflect the NSS situation more comprehensively, timely and accurately.