Network security situation assessment is an important means of understanding the current network security situation to provide a basis for taking security measures. To address the problem that the accuracy of existing network security situation assessment methods needs to be improved, this paper proposes a network security situation assessment method based on support vector machine (SVM) optimized by whale optimization algorithm (WOA) that is improved by adaptive weight (AW) combined with simulated annealing algorithm (SA). In this method, the SVM is embedded into the fitness function calculation of the improved WOA, and the global optimization characteristics of WOA are used to determine the optimal penalty parameter $c$ and kernel function parameter $g$ of the SVM. To solve the problem of the WOA being prone to falling into local extremum and slow convergence when solving large and complex data problems, an adaptive weight is used to adjust the whale position update coefficient, and a simulated annealing algorithm (SA) is used to increase random search factors to avoid falling into local extremum, so as to improve the global optimization ability. The experimental results show that this method is feasible, can assess the network security situation more accurately, and has better convergence than other assessment algorithms based on an improved SVM.

Network-security situation prediction is a crucial aspect in the field of network security. It is primarily achieved through monitoring network behavior and identifying potential threats to prevent and respond to network attacks. In order to enhance the accuracy of situation prediction, this paper proposes a method that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU), while also incorporating an attention mechanism. The model can simultaneously handle the spatial and temporal features of network behavior and optimize the weight allocation of features through the attention mechanism. Firstly, the CNN’s powerful feature extraction ability is utilized to extract the spatial features of the network behavior. Secondly, time-series features of network behavior are processed through the GRU layer. Finally, to enhance the model’s performance further, we introduce attention mechanisms, which can dynamically adjust the importance of different features based on the current context information; this enables the model to focus more on critical information for accurate predictions. The experimental results show that the network-security situation prediction method, which combines a CNN and a GRU and introduces an attention mechanism, performs well in terms of the fitting effect and can effectively enhance the accuracy of situation prediction.

Accurately extracting network security situation elements is an important basis for improving the situational awareness of industrial Internet security. This paper proposes an industrial internet security situation element extraction algorithm based on a hybrid neural network. Firstly, the powerful local feature extraction ability of convolutional neural networks (CNNs) was used to extract the features of key situation elements, and the obtained features were flattened and then input into long short-term memory networks (LSTMs) to solve the problem of the poor time feature extraction ability of CNNs. Then, the output features of the fully connected layer were input to the backpropagation (BP) network for classification, and LSTM was used to correct the prediction residual of the BP network to optimize the parameters of each module in the model and improve the classification effect and generalization ability. Comparative experimental results show that the accuracy of the model on the KDD Cup99 dataset and SCADA2014 dataset can reach 98.03% and 98.96%, respectively. Compared with other models, the model has higher classification accuracy and can provide more effective indicator data for security situation assessment.

Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods used for this purpose, adversarial example generation methods for deep learning models have become a hot topic in academic research. To overcome problems such as extensive network access, high attack costs, and limited universality in generating adversarial examples, this paper proposes a generic algorithm for adversarial example generation based on improved DE-C&W. The algorithm employs an improved differential evolution (DE) algorithm to conduct a global search of the original examples, searching for vulnerable sensitive points susceptible to being attacked. Then, random perturbations are added to these sensitive points to obtain adversarial examples, which are used as the initial input of C&W attack. The loss functions of the C&W attack algorithm are constructed based on these initial input examples, and the loss function is further optimized using the Adaptive Moment Estimation (Adam) algorithm to obtain the optimal perturbation vector. The experimental results demonstrate that the algorithm not only ensures that the generated adversarial examples achieve a higher success rate of attacks, but also exhibits better transferability while reducing the average number of queries and lowering attack costs.