Cross-site scripting (XSS) attacks pose a significant threat to web applications and user privacy, with the number of such attacks rapidly increasing. Although existing machine learning and deep learning-based XSS attack detection models are effective against common XSS attacks, these models all overlook their own security and often fail to defend against adversarial samples that exploit model vulnerabilities, allowing attackers to successfully bypass these models by using XSS adversarial samples. To address this challenge, in this paper, we propose a novel XSS attack detection model based on two-stage Abstract Syntax Tree (AST) analysis and Long Short-Term Memory (LSTM) neural networks, effectively mitigating the impact of adversarial samples. Our model leverages the ability of AST parsing and analysis of HTML and JavaScript code to effectively eliminate redundant information and adversarial perturbations introduced by adversarial samples. The two-stage process first extracts JavaScript code from the HTML AST, then identifies malicious code fragments from the JavaScript AST. Finally, the LSTM neural network is trained to classify samples as malicious or benign. By analyzing the HTML and JavaScript components of web pages, our model identifies and eliminates adversarial perturbations that interfere with detection, significantly enhancing the security and reliability of the detection process. Extensive experiments on real datasets demonstrate our model's superior performance, achieving an accuracy rate of 0.991 and an F1 score of 0.998 against standard XSS samples, outperforming existing models. More importantly, when facing adversarial XSS samples, most existing detection models exhibit severe robustness degradation with the detection rate (DR) below 0.880, whereas our model maintains a detection rate of over 0.982, significantly higher than state-of-the-art models and demonstrating its significant effectiveness in defending against XSS adversarial attacks.

Privacy-preserving record linkage (PPRL) aims to link records of the same real-world entity from different databases without exposing any private information about the entity. Bloom filters are widely used in PPRL due to their effectiveness in encoding records while enabling fast approximate linkage in the case of attribute value errors and changes. However, the basic Bloom filters used for PPRL can be subject to cryptanalysis attacks that expose the plain-text values encoded in them. Recent studies have successfully attacked some improved Bloom filter encodings in PPRL but require specific conditions or knowledge of various encoding parameters to obtain high accuracy. This paper presents a novel attack based on differential analysis against Bloom filters used for PPRL. The attack exploits graphs to model the relationship between attribute value variation and the difference between Bloom filters. Then, features are generated for the node in graphs according to a clustering algorithm that we propose. Thus, we can match nodes with similar features to re-identify encoded records. Experiments on two real-world databases show that even with improved Bloom filter encoding and some hardening techniques, our attack can re-identify private information from encoded records with high accuracy and require less priori knowledge.

The open standard (Web Mapping Service, WMS) promulgated by OGC has enabled the share and interoperation of web based mapping. This paper presented the design and implementation of an integrated WMS portal based on existing WMS. The technical and functional designs are detailed on the following components: WMS viewer in the client, gazetteer service, remote data transferring and processing, geospatial database and its automatic update. We also discussed how these components are integrated seamlessly through service chain to provide a highly efficient WMS portal.

This paper study topographic attributes extraction and analysis methods based on grid DEMs. We used mathematic statistics method and comparative analysis to analyze effect of DEMs with different resolutions on the outcome of topographic terrain extraction, including slope, topographic index (i.e. ln(alpha/tanbeta)), watershed area, and drainage network. The results show that effect of DEMs with different resolution on topographic attributes is different. Terrain is smoothed, the mean slope decreases, the mean value of ln(alpha/tanbeta) distribution increases, the total area of watershed increases, the count of subwatersheds decrease, the total length of streams decrease, and the drainage density decreases as DEMs grid size increase.