As a result of the expansions that have taken place in the field of networking and the increase in the number of users of networks, there have recently been breakthroughs made in the techniques and methods used for network security. In this paper, a virtual private network (VPN) is proposed as a means of providing the necessary level of security for particular connections that span across vast networks. After the network performance metrics such as time delay and throughput have been accomplished, the suggested VPN is recommended for the purpose of assuring network security. In addition, artificial intelligence attack predictors and virtual private networks have been implemented with the purpose of preventing harmful activity within such connections. Using a wide variety of machine learning methods like Random Forests and Nave Bays, malicious assaults of any kind can be identified and thwarted in their tracks. Another technique for anticipating attacks is the use of an artificial neural network, which is a type of system that engages in deep learning and learns the behaviors of attacks while it is being trained so that it can then predict attacks. The results of this study demonstrate that the use of machine learning and artificial intelligence techniques can significantly improve the security and performance of virtual private networks and can effectively identify and prevent malicious attacks on networks.

Face detection is the most critical and first step in the attendance management system. The human face is non-rigid and has many differences in visual situations, scale, clarity, poses, and rotation. The precise and reliable identification was a challenge for the researcher. A variety of methods and techniques are suggested, but due to many variations, no one technique is very effective for all sorts of faces and pictures. Many techniques show good results under certain conditions, and others are successful for various types of images. Image-discriminating methods are commonly used for pattern and image analysis. Specific forms of prejudice are discussed.

Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used