Early Detection of Network Attacks Based on Weight-Insensitive Neural Networks

Abstract

In this paper, we describe an approach for the early detection of network attacks using weight-insensitive neural networks (or weight agnostic neural networks (WANNs). The selection of the type of neural networks is determined by the specifics of their architecture, which provides high data-processing speed and performance, which is significant when solving the problem of the early detection of attacks. The experimental studies demonstrate the effectiveness of the proposed approach, which is based on a combination of multiple regression for selecting features of the training set and WANNs. The accuracy of attack recognition is comparable to the best results in this field with a significant gain in time.