Prediction of cyber-attacks in air transport using neural networks

M. Izdebski, Anna Michalska, Ilona Jacyna-Gołda, L. Gherman
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引用次数: 0

Abstract

This article addresses the topic of cyber-attacks in air transport, which is crucial for ensuring the safety and reliability of airports and air transport operations. The aim of the article was to present a new method for predicting cyber-attacks in air transport based on neural networks. The task of the neural network was to determine the multiple regression function based on which the probability of a cyberattack occurring at a specified hour and on a specific day of the week is predicted. The probability, depending on the time of the cyberattack occurrence, was determined using theoretical distributions. The method was verified with real data. Verification of the method confirmed its high effectiveness, determined at the level of 92%. The study examined the effectiveness of using the classical multiple regression method in predicting cyber-attacks in air transport. The classical multiple regression model covered only 0.14 of the input data, while the regression model generated by the neural network covered 0.99, indicating the high efficiency of the developed neural network.
利用神经网络预测航空运输中的网络攻击
本文讨论的主题是航空运输中的网络攻击,这对确保机场和航空运输业务的安全性和可靠性至关重要。文章旨在介绍一种基于神经网络的航空运输网络攻击预测新方法。神经网络的任务是确定多元回归函数,在此基础上预测网络攻击在特定时间和特定日期发生的概率。根据网络攻击发生的时间,利用理论分布确定概率。该方法通过真实数据进行了验证。验证结果表明,该方法非常有效,有效率达到 92%。该研究检验了使用经典多元回归法预测航空运输网络攻击的有效性。经典的多元回归模型仅覆盖了 0.14% 的输入数据,而神经网络生成的回归模型覆盖了 0.99%,这表明所开发的神经网络具有很高的效率。
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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