Network Security Situational Awareness Based on Improved Particle Swarm Algorithm and Bidirectional Long Short-Term Memory Modeling

With the continuous development of information technology, network security risks are also rising, and the ability to quickly perceive network threats has become an important prerequisite and an important means to cope with network risks. Currently, there are various types of network attacks and complex attacking techniques, and the large differences between them have led to the difficulty of collecting and recognizing the common characteristics of network attacks. Considering the regular temporal fluctuations in network attacks, a method for network security situational awareness, based on an enhanced Particle Swarm Optimization Bidirectional Long Short-Term Memory (BiLSTM) network model, is proposed. By gathering and organizing critical information within the network, an encapsulated Wrapper feature selection algorithm is utilized for the extraction of element features. The refined Particle Swarm Optimization algorithm is applied to optimize the parameters of the BiLSTM network, enabling a rapid convergence and enhancing the training efficiency, thus effectively identifying the categories of network attacks. The experimental results show that the MAPE for the proposed model has been diminished to 0.36%, while the sMAPE is 2.654%. Additionally, the fitting coefficient attains a value of 0.92. This indicates a high level of recognition and precision exhibited by the proposed model in detecting network security risk behaviors. Furthermore, in contrast to the traditional CNN neural network, the proposed model is more compact, which significantly reduces the computational overhead and allows for efficient network security situational awareness.