With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people. However, due to the lack of an effective preventive mechanism, the police are often in a passive position. Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data. First, we perform preliminary screening and cleaning based on the keyword “defrauded” to obtain valid fraudulent user Identity Documents (IDs). The basic information and account information of these users is user-labeled to achieve the purpose of distinguishing the types of fraud. Secondly, through feature engineering technologies such as avatar recognition, Artificial Intelligence (AI) sentiment analysis, data screening, and follower blogger type analysis, these pictures and texts will be abstracted into user preferences and personality characteristics which integrate multi-dimensional information to build user portraits. Third, deep neural network training is performed on the cube. 80% percent of the data is predicted based on the N-way K-shot problem and used to train the model, and the remaining 20% is used for model accuracy evaluation. Experiments have shown that Few-short learning has higher accuracy compared with Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN). On this basis, this paper develops a WeChat small program for early warning of telecommunications network fraud based on user portraits. When the user enters some personal information on the front end, the back-end database can perform correlation analysis by itself, so as to match the most likely fraud types and give relevant early warning information. The fraud warning model is highly scaleable. The data of other Applications (APPs) can be extended to further improve the efficiency of anti-fraud which has extremely high public welfare value.

Structured Query Language (SQL) injection attacks have become the most common means of attacking Web applications due to their simple implementation and high degree of harm. Traditional injection attack detection techniques struggle to accurately identify various types of SQL injection attacks. This paper presents an enhanced SQL injection detection method that utilizes content matching technology to improve the accuracy and efficiency of detection. Features are extracted through content matching, effectively avoiding the loss of valid information, and an improved deep learning model is employed to enhance the detection effect of SQL injections. Considering that grammar parsing and word embedding may conceal key features and introduce noise, we propose training the transformed data vectors by preprocessing the data in the dataset and post-processing the word segmentation based on content matching. We optimized and adjusted the traditional Convolutional Neural Network (CNN) model, trained normal data, SQL injection data, and XSS data, and used these three deep learning models for attack detection. The experimental results show that the accuracy rate reaches 98.35%, achieving excellent detection results.

In this paper, considering the cost of base station, coverage, call quality, and other practical factors, a multi-objective optimal site planning scheme is proposed. Firstly, based on practical needs, mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives, coverage objectives, and quality objectives. Then, a multi-objective optimization model was established by combining threshold and traffic volume constraints. In order to reduce the time complexity of optimization, a non-dominated sorting genetic algorithm (NSGA) is used to solve the multi-objective optimization problem of site planning. Finally, a strategy for clustering and optimizing weak coverage areas was proposed. In order to avoid redundant neighborhood retrieval during cluster expansion, the Fast Density-Based Spatial Clustering of Applications with Noise (FDBSCAN) clustering method was adopted. With different sub-objectives as the main objectives, this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations, as well as relevant site planning maps, and provided three planning schemes for different main objectives. The simulation results show that the traffic coverage of the three station planning schemes is above 90%. The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.