Abstract
Abstract Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.
Highlights
In the era of big data, the network can collect various types of data formats, including network device log, security device log and information running in the service system
This paper proposes a network security situation assessment method based on linear programming (LP) combined with AHP method combined with improved GSA optimised FNN
To solve the inability to effectively deal with the complexity of the network system data and the analytic hierarchy process requires expert experience to obtain a judgement matrix
Summary
In the era of big data, the network can collect various types of data formats, including network device log, security device log and information running in the service system. The information age should have more sources for network security situation awareness than the past. Another feature of big data is the rapid processing of massive data. There are four main aspects of network security analysis: first of all, through the study of network attack cases, establishing a knowledge base, including principles, characteristics, environment, methods and the most commonly used equipment. The knowledge base of environmental vulnerabilities is established by analysing the system vulnerabilities. By analysing the architecture topology and equipment, the environmental threat knowledge base is established [6,7,8]. Security status evaluation elements are generated, including security threats, vulnerabilities and operating conditions [9, 10]
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