The Impact of Data Injection on Predictive Algorithm Developed within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity

Abstract

Cybersecurity plays a relevant role in the new digital age within the 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 for cybersecurity is required. The four main categories to classify threats are interruption, fabrication, modification, and interception. They all share a common thing, which is to soften the three pillars that cybersecurity needs to guarantee. These pillars are confidentiality, availability, and integrity of data (CIA). Data injection can contribute to this event by the creation of false indicators, which can lead to error creation during the manufacturing engineering processes. In this paper, the impact of data injection on the existing dataset used in manufacturing processes is described. 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 cluster 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 were carried out using a real dataset related to the electrical harnesses installed in a C295 military aircraft, estimate that injection of false data indicators increases the probability of creating an error by 24.22% based on the predicted outcomes required for the generation of the manufacturing processes. Overall, implementing cybersecurity measures and advanced methodologies to detect and prevent cyberattacks is necessary.

Keywords

• predictive algorithms
• cybersecurity
• machine learning
• advanced persistent threats