The paper is devoted to the actual problem of ensuring the information security of web-sites. It discusses a method for detecting intentional threats to the confidentiality of information as a result of unauthorized access and is manifested in the form of atypical requests to resources by users. The paper proposes a method based on data mining. Its essence lies in the classification of users' behavior on the basis of information about their actions using an artificial neural network. As a basis for the implementation of the proposed tools, site security audit tools are used as a source of information. The structure of neural network, training methods and practical application are described, and the effectiveness of the proposed methods is evaluated.
Keywords: Data mining, artificial neural network, web-design, machine learning, classification
The article is devoted to the problem of using artificial neural networks as one of the methods of data mining (intellectual data analysis) for classifying the competencies of university students when determining the educational specialization. It describes the complexity of the process of selecting specialization and the associated negative consequences, as well as an approach to solving the given problem using software classification tools. As a basis for the implementation of the proposed tools, the student's information system is used on the website of the University of Tartus, Syria. The article presents data selection criteria to form the training sample, which includes the academic performance in some courses as input vectors. The values of the output vector depend on completed specialization and it get into the training set only if it is properly selected. On the basis of these data, the structure of a multilayer artificial neural network is formed and the learning algorithm is selected, the results of which are reflected on the university's website in the form of advice on the choice of future specialization, which has allowed increase the effectiveness of the educational process.
Keywords: Data mining, artificial neural network, web-design, machine learning, classification