DEVELOPMENT OF A NEURAL NETWORK STRUCTURE FOR INTRUSION DETECTION ANALYSIS

Abstract

The right security solutions in the information and communication world are critical to network security by providing real-time network protection against network vulnerabilities and data usage. An effective intrusion detection strategy is able to use a holistic approach to protect critical systems from unauthorized access or attacks. The paper examines the latest scientific achievements and research related to the analysis of detection of network intrusions using machine learning (ML) methods. The article describes a complex security solution based on machine learning (ML) for network intrusion detection using a complex controlled ML structure and ensemble feature selection methods. In addition, a comparative analysis of several MH models and function selection methods is provided. The article develops a general mechanism for detecting and achieving higher accuracy with a minimum frequency of false positive results (FPR). The paper uses datasets and the results show that the detection model can successfully identify 99.3% of intrusions with the lowest error rate of 0.5%, which shows better performance compared to existing solutions. The article combines the selection of ensemble functions and ensemble machine learning approaches as a detection mechanism in SBB to detect network anomalies. An experimental study was conducted with feature sets obtained from nine feature selection methods, and then these feature sets were combined to obtain the minimum number of features using majority voting. A comparative analysis of sets of functions was carried out. Controlled methods are used, which are more efficient with a balanced data set. To make the training dataset balanced, the data type (benign or attacking) with the minimum number of data instances in that training dataset was first selected. An ensemble feature selection and ensemble classification algorithm is implemented to improve the overall performance of the proposed machine learning model. Prospects for the development of further research are proposed.