Artificial Intelligence (AI) and Machine Learning (ML) applications are currently found across all engineering domains, including cybersecurity engineering. This paper presents an overview of the AI-systems and technologies that can greatly benefit the security domain and its potential solutions for the aviation industry: anomaly detection for avionics, securing data link communications, and security certification, among others.As showcased in the "National Strategy for Aviation Security" report in December 2018 and the U.S. Government Accountability Office report "Aviation Cybersecurity" in October 2020, there is a need for resilient cybersecurity practices and approaches to address the current issues that aviation faces today. Designing and implementing solutions to address these issues without exploring the feasibility of harnessing AI-powered cybersecurity tools would overlook the potential advantages these technologies can offer.Therefore, this paper aims to provide a roadmap to adapt well-known ML-cybersecurity approaches to aviation security engineering and airworthiness: (i) autonomous and semi-autonomous cybersecurity for autonomous flight operations security, (ii) game theory models for adversarial and uncertainty modeling, (iii) human-AI interfaces for airport security monitoring and decision-making assistance, (iv) predictive analytics for anomaly detection for avionics and e-enabled aircraft, and (v) AI-based reasoning trustworthiness for software reliability and security certification. This paper also presents the challenges of including AI-cybersecurity in the aviation ecosystem to ground the proposed solutions within an accepted set of industry regulations, such as design verification for AI/ML algorithms and certification specifications for AI/ML solutions for manufacturers and agencies.
Artificial Intelligence and Machine Learning Approaches For Aviation Cybersecurity: An Overview
20.04.2021
3263345 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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