Cross-site scripting (XSS) is one of the most common security threats to web applications, posing a serious challenge to network information security. Targetting the limitations of traditional detection methods in identifying complex XSS attacks, this paper proposes a hybrid deep learning model that integrates convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism. The model captures local attack feature patterns through the CNN layer, learns contextual long-term dependencies through the BiLSTM layer, and introduces a multi-head attention mechanism to enhance the focus on key attack vectors. In the preprocessing stage, an improved regular word segmentation algorithm is used to construct semantic feature vectors, which effectively solves the problem of text feature representation of XSS attacks. Experimental results show that compared with the baseline method, the proposed method achieves an accuracy of 0.9938, a precision of 0.9936, a recall of 0.9936, and an F1-score of 0.9937 on real datasets. This shows that by integrating CNN and BiLSTM features and combining the attention mechanism, the model can effectively deal with complex XSS attacks.

To address the challenges of heterogeneous desensitization requirements, limited system resources, and inefficient resource allocation for structured sensitive data, this paper proposes a data security-oriented multi-objective optimization method for desensitization resource allocation of civil aviation passenger sensitive data. The proposed method integrates heterogeneous resource scheduling with multi-objective optimization to achieve adaptive resource allocation under resource-constrained environments. A clustering-based approach is employed to group desensitization tasks with similar computational characteristics, and a personalized sensitivity coefficient is introduced to characterize task complexity and guide adaptive resource allocation. Based on task-group characteristics, a multi-objective optimization framework integrating the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed to jointly optimize desensitization execution time, computational resource consumption, and task-group matching cost. Experimental results show that the proposed method successfully optimizes 92.76% of desensitization tasks, while 31.59% achieve optimal solutions with fewer thread resources than the original configuration. Hyperparameter sensitivity analysis further identifies an effective parameter configuration that provides a favorable balance between optimization quality and computational efficiency. Comparative experiments with five representative multi-objective optimization algorithms further verify the superiority and robustness of the proposed method.

To address the challenges of complex, heterogeneous, and blurred sensitivity boundaries in the sensitive data sources of civil aviation passengers, this paper proposes a hierarchical measurement method. This model integrates information entropy and random forest, achieving measurable sensitivity. Firstly, the correlation between data sensitivity level and business characteristics is established. Then, a Random Forest-based Hierarchical Measurement with Sensitivity Information Content Analysis (RF-HM-SICA) model integrating information entropy and random forest is proposed to construct a sensitivity measurable hierarchical measurement method for passenger sensitive data. The experimental results show that the RF-HM-SICA model exhibits high stability, generalization capability, and boundary sample protection ability under different data sizes and sensitivity levels, making it suitable for solving the multidimensional heterogeneity measurement problem of sensitive data of civil aviation passengers and providing support for data security sharing protection. In particular, the recognition accuracy and precision for high-sensitivity data approach 1.0 across datasets of different scales, while RF-HM-SICA exhibits the lowest misclassification rate among all compared models.