Prediction of cyber-attacks in air transport using neural networks

• Autorzy: Izdebski Mariusz , Michalska Anna , Jacyna-Gołda Ilona , Gherman Laurian

Identyfikatory

DOI

10.17531/ein/191476

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

cyber-attack; neural networks; air transport; prediction

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 4
• Strony: art. no. 191476
• Opis fizyczny: Bibliogr. 38 poz., tab., wykr.

Twórcy

autor

Izdebski Mariusz

• Warsaw University of Technology, Poland

• https://orcid.org/0000-0002-9157-7870

autor

Michalska Anna

• Polish Air Force University, Poland

• https://orcid.org/0000-0002-9292-589X

autor

Jacyna-Gołda Ilona

• Warsaw University of Technology, Poland

• https://orcid.org/0000-0001-8840-3127

autor

Gherman Laurian

• Henri Coandă Air Force Academy, Romania

• https://orcid.org/0000-0003-0315-629X

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