PROSPECTIVE DIRECTIONS OF TRAFFIC ANALYSIS AND INTRUSION DETECTION BASED ON NEURAL NETWORKS

The main problems of the network security at the moment are the difficulty of combining existing systems from different vendors and ensuring their stable interaction with each other. Intrusion detection is one of the main tasks of a proper level of network security, because it is they who notify about attacks and can block them when detected. Today, monitoring and analyzing the quality of traffic in the network, detecting and preventing intrusions is helped by IDS systems and IDS systems of the new generation IPS. However, they have been found to have certain drawbacks, such as the limitations of signature-based systems, as static attack signatures limit the flexibility of systems and pose the threat of missing detection of other attacks not entered into the database. This gives rise to the creation of more and more new hybrid systems, but the challenge is to ensure their efficiency and flexibility, which is helped by the use of artificial neural networks (ANNs). This paper considers ways to improve the use of the convolutional neural network model itself by means of modified processing, data analysis, the use of Softmax and FocalLoss functions to avoid the problem of uneven distri