Detecting Attacks on Computer Networks Using Artificial Intelligence Algorithms

Lukasz Krolik, M. Kedziora, Jolanta Mizera-Pietraszko, I. Józwiak
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Abstract

This paper presents a neural network model which was developed and trained to detect attacks on computer networks and to identify the differences indicating what distinguishes them from the regular network traffic. The influence of some parameters on the operation of the network was examined in order to select those characteristic for cyberattack. A certain number of models was tested for binary and multi-class classification. The accuracy of recognition by the network was evaluated for both the entire set and individual categories of the network traffic. The results obtained are promising based on comparison to those published in related work studies on intrusion detection systems.
利用人工智能算法检测计算机网络攻击
本文提出了一种神经网络模型,该模型被开发和训练用于检测计算机网络上的攻击,并识别出它们与常规网络流量的区别。研究了一些参数对网络运行的影响,以便选择适合网络攻击的特征。对一定数量的模型进行了二分类和多分类测试。对网络流量的整个集合和单个类别进行了识别的准确性评估。通过与已发表的入侵检测系统相关研究成果的比较,得到了较好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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