Biblio

With the rapid development of information technology, hacker invasion, Internet fraud and privacy disclosure and other events frequently occur, therefore information security issues become the focus of attention. Protecting the secure transmission of information has become a hot topic in today's research. As the carrier of information, image has the characteristics of vivid image and large amount of information. It has become an indispensable part of people's communication. In this paper, we proposed the key simulation analysis research based on M-J set. The research uses a complex iterative mapping to construct M set. On the basis of the constructed M set, the constructed Julia set is used to form the encryption key. The experimental results show that the generalized M-set has the characteristics of chaotic characteristic and initial value sensitivity, and the complex mapping greatly exaggerates the key space. The research on the key space based on the generalized M-J set is helpful to improve the effect of image encryption.

With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types.