Version 1
: Received: 26 September 2023 / Approved: 26 September 2023 / Online: 27 September 2023 (02:38:03 CEST)
How to cite:
Bautista-Hernández, J.; Martín-Prats, M.Á. The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity. Preprints2023, 2023091812. https://doi.org/10.20944/preprints202309.1812.v1
Bautista-Hernández, J.; Martín-Prats, M.Á. The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity. Preprints 2023, 2023091812. https://doi.org/10.20944/preprints202309.1812.v1
Bautista-Hernández, J.; Martín-Prats, M.Á. The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity. Preprints2023, 2023091812. https://doi.org/10.20944/preprints202309.1812.v1
APA Style
Bautista-Hernández, J., & Martín-Prats, M.Á. (2023). The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity. Preprints. https://doi.org/10.20944/preprints202309.1812.v1
Chicago/Turabian Style
Bautista-Hernández, J. and María Ángeles Martín-Prats. 2023 "The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity" Preprints. https://doi.org/10.20944/preprints202309.1812.v1
Abstract
Cybersecurity plays a relevant role in the new digital age in aerospace industry. Predictive algorithms are necessary to interconnect complex systems within the cyberspace. In this context, where security protocols do not apply, challenges to maintain data privacy and security arise for the organizations. Thus, the need of cybersecurity is required. The four main categories to classify threats are interruption, fabrication, modification and interception. They all share a common thing, soften the three pillars which cybersecurity needs to guarantee. These pillars are confidentiality, availability and integrity of data (CIA). Data injection can contribute to this event by creation of false indicators which can lead to errors creation during the manufacturing engineering process. In this paper, the impact of data injection on existing dataset used on manufacturing process is shown. The design model synchronizes the following mechanisms developed within machine learning techniques which are, the risk matrix indicator to assess the probability of producing an error, the dendrogram to clusters the dataset in groups with similarities, the logistic regression to predict the potential outcomes and the confusion matrix to analyze the performance of the algorithm. The results presented in this study, which was carried out using a real dataset related to the electrical harnesses installed in a C295 military aircraft, estimate that injection of false data indicators increase the probability of errors creation in 24.22 % on the predicted outcomes required for the generation of the manufacturing process. Overall, implementing cybersecurity measures and advanced methodologies to detect and prevent cyberattacks are necessary.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
