Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems

Ostonya Thomas, Muhaimin Bin Munir, Jean-Michel Tine, Mizanur Rahman, Yuchen Cai, Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan, Trayce Hockstad, Mashrur Chowdhury

Abstract:

Technological advancements have revolutionized numerous industries, including transportation.
While digitalization, automation, and connectivity have enhanced safety and efficiency, they
have also introduced new vulnerabilities. With 95% of data breaches attributed to human error,
promoting cybersecurity awareness in transportation is increasingly critical. Despite numerous
cyberattacks on transportation systems worldwide, comprehensive and centralized records of
these incidents remain scarce. To address this gap and enhance cyber awareness, this paper
presents a large language model (LLM)-based approach to extract and organize transportationrelated cyber incidents from publicly available datasets. A key contribution of this work is the
use of generative AI to transform unstructured, heterogeneous cyber incident data into structured
formats. Incidents were sourced from the Center for Strategic & International Studies (CSIS) List
of Significant Cyber Incidents, the University of Maryland Cyber Events Database (UMCED),
the European Repository of Cyber Incidents (EuRepoC), the Maritime Cyber Attack Database
(MCAD), and the U.S. DOT’s Transportation Cybersecurity and Resiliency (TraCR) Examples
of Cyber Attacks in Transportation (2018 to 2022). These were classified by a fine-tuned LLM
into five transportation modes: aviation, maritime, rail, road, and multimodal, forming a
transportation-specific cyber incident database. Another key contribution of this work is the
development of a Retrieval Augmented Generation question-answering system, designed to
enhance accessibility and practical use by enabling users to query the curated database for
specific details on transportation-related cyber incidents. By leveraging LLMs for both data
extraction and user interaction, this study contributes a novel, accessible tool for improving
cybersecurity awareness in the transportation sector.