Network security situation evaluation method based on CS and improved BP neural network

Abstract

The invention relates to a network security situation evaluation method based on CS and improved BP neural network. The method comprises four steps of S1. acquiring network security situation elements, forming a training sample set and a test sample set, and determining a BP neural network structure; S2. seeking an optimal initial weight and a threshold by using a CS algorithm; S3. introducing a momentum factor and a gradient factor to improve the BP neural network; S4. training the improved BP neural network, finally, using the trained network in network security situation evaluation so as toobtain a final situation value and a security level. Network security situation is evaluated precisely and quantitatively by using the improved BP neural network, so that subjective effects of expertopinions in traditional evaluation methods are lowered, and overall network security situation is reflected objectively and comprehensively; and the network security situation is improved by combining the CS algorithm and introducing the momentum factor and gradient factor, the convergence speed is improved, time and space overheads are reduced, and accuracy and practicability of network securitysituation evaluation are improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a network security situation assessment method based on CS and improved BP neural network. Background technique [0002] With the rapid development of Internet technology, the scale of the network has gradually increased and become more complex, the attacks suffered are diversified, the security incidents have increased sharply, and security issues have become increasingly prominent and urgent. In this context, network security situation assessment emerged as the times require, and has gradually become the research focus of next-generation network security technologies. Comprehensive analysis and real-time assessment of the situation can help managers grasp the overall network security situation, provide guidance for network security management command and decision-making, and minimize risks and losses. [0003] At present, the network security situation assessment at h...

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Problems solved by technology

[0004] The first two methods are widely used, but there are shortcomings such as strong subjective dependence, difficulty in obtaining prior knowledge, and inappropriate dynamic and complex network environments. In contrast, the evaluation method based on pattern recognition has higher accuracy and objectiveness. BP neural network is a multi-layer feed-forward network, which is widely used in situation assessment because of its powerful self-learning ability, good generalization ability and fault tolerance ability. However, from the perspective of application effects, there are still the following deficiencies: (1) The evaluation data source is single, resulting in one-sided evaluation results and low credibility

(2) The space-time overhead is large, does not meet the real-time requirements and the evaluation results are not accurate enough (3) The traditional BP neural network has disadvantages such as easy to form local minimum, easy to oscillate, and slow convergence speed

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