Application Of Fractal Neural Network In Network Security Situation Awareness

Abstract

The purposes are to accurately perceive the network security situations and predict the development trend and effectively defend against network attacks during the operation of the Internet. The Long Short-Term Memory (LSTM) network is adopted as the subject of the network security situation awareness and prediction model. Moreover, it is optimized by the Genetic Algorithm (GA) to improve its global search capability. Then, a Fractal Neural Network (FNN) is constructed in combination with fractal theory, which is utilized in network security situation awareness to avoid the exploding or vanishing gradient problems. The KDD CUP 99 standard dataset is applied to verify the performance of the proposed GA-LSTM FNN; results demonstrate that its accuracy of network security situation awareness can reach 90.22%. The experimental results confirm that using the fractal difference function as the activation function can deliver the gradient variation in a balanced and stable manner. Besides, it can improve the feasibility and effectiveness of the neural network structure for network security situation awareness and prediction. The FNN studied is of practical significance for assessing the current network security situation and predicting its evolution trend, providing a reference for protecting the operation of the Internet from network attacks.