DATA CLUSTERING ALGORITHM FOR PROTECTING CONFIDENTIAL INFORMATION ON THE INTERNET

Abstract

The article is devoted to solving the scientific problem of protecting confidential information in the Internet based on the algorithm for clustering significant amounts of data. The protection of a computer network confidential information is a hot topic for research, especially in connection with the growing use of information technology and the increase in data of valuable information stored in the Internet. With the growth of information responsibility, the need for effective methods of computer networks information security has become critical. In this scientific article, the authors propose a solution to the problem of protecting computer networks confidential information based on the big data clustering algorithm. Traditional intrusion detection methods have limitations such as the ability to work only with one- or two-dimensional data, and also have a strong reliance on prior knowledge. To eliminate these limitations, the authors propose a heuristic intrusion detection algorithm that uses clustering based on a cloud model. The proposed algorithm takes advantage of both labeled and unlabeled samples for data clustering, thereby reducing reliance on a priori knowledge. The results of a computational experiment carried out on the proposed algorithm were compared with several canonical intrusion detection algorithms. The results showed that the proposed algorithm improved the performance of the intrusion detection system, increased the accuracy of detection, reduced the false alarm rate, and enhanced the reliability of the system. The dynamic weighting method used in the algorithm removed the complexity of highlevel data processing and allowed the algorithm to learn itself, resulting in a relatively stable cloud model. Despite the significant improvement in the performance of the proposed algorithm compared to the canonical clustering algorithms, the results of the study also showed that the algorithm has some limitations, such as a high false positive rate and sensitivity to data with certain types of distribution. To eliminate these shortcomings, further improvement of the algorithm is required. In general, the proposed heuristic clustering intrusion detection algorithm based on the cloud model is a promising solution for protecting computer networks confidential information.