A dataset for cyber threat intelligence modeling of connected autonomous vehicles

TL;DR

This paper introduces a new annotated dataset of automotive cybersecurity reports to facilitate cyber threat intelligence modeling for connected autonomous vehicles, addressing the lack of such resources in the automotive cybersecurity domain.

Contribution

The creation and annotation of a comprehensive automotive cybersecurity dataset with real reports, entities, and relations for cyber threat intelligence research.

Findings

Dataset contains 908 reports with 3678 sentences and 8195 security entities.

Analysis of cyber threat intelligence algorithms using the dataset.

Dataset serves as a benchmark for evaluating cybersecurity modeling methods.

Abstract

Cyber attacks have become a vital threat to connected autonomous vehicles in intelligent transportation systems. Cyber threat intelligence, as the collection of cyber threat information, provides an ideal approach for responding to emerging vehicle cyber threats and enabling proactive security defense. Obtaining valuable information from enormous cybersecurity data using knowledge extraction technologies to achieve cyber threat intelligence modeling is an effective means to ensure automotive cybersecurity. Unfortunately, there is no existing cybersecurity dataset available for cyber threat intelligence modeling research in the automotive field. This paper reports the creation of a cyber threat intelligence corpus focusing on vehicle cybersecurity knowledge mining. This dataset, annotated using a joint labeling strategy, comprises 908 real automotive cybersecurity reports, containing 3678 sentences, 8195 security entities and 4852 semantic relations. We further conduct a comprehensive analysis of cyber threat intelligence mining algorithms based on this corpus. The proposed dataset will serve as a valuable resource for evaluating the performance of existing algorithms and advancing research in cyber threat intelligence modeling within the automotive field.