Proactive security defense: cyber threat intelligence modeling for connected autonomous vehicles (2410.16016v1)

Abstract: Cybersecurity has become a crucial concern in the field of connected autonomous vehicles. Cyber threat intelligence (CTI), as the collection of cyber threat information, offers an ideal way for responding to emerging cyber threats and realizing proactive security defense. However, instant analysis and modeling of vehicle cybersecurity data is a fundamental challenge since its complex and professional context. In this paper, we suggest an automotive CTI modeling framework, Actim, to extract and analyse the interrelated relationships among cyber threat elements. Specifically, we first design a vehicle security-safety conceptual ontology model to depict various threat entity classes and their relations. Then, we manually annotate the first automobile CTI corpus by using real cybersecurity data, which comprises 908 threat intelligence texts, including 8195 entities and 4852 relationships. To effectively extract cyber threat entities and their relations, we propose an automotive CTI mining model based on cross-sentence context. Experiment results show that the proposed BERT-DocHiatt-BiLSTM-LSTM model exceeds the performance of existing methods. Finally, we define entity-relation matching rules and create a CTI knowledge graph that structurally fuses various elements of cyber threats. The Actim framework enables mining the intrinsic connections among threat entities, providing valuable insight on the evolving cyber threat landscape.

• The paper introduces the Actim framework, a proactive model that constructs an automotive CTI knowledge graph to fuse diverse cybersecurity threat elements.
• The methodology leverages a BERT-DocHiatt-BiLSTM-LSTM architecture with cross-sentence context to extract 8195 entities and 4852 relationships from 908 CTI texts.
• The findings offer actionable insights for enhancing security defenses in connected autonomous vehicles and guide future research in automotive cybersecurity.

Proactive Security Defense: Cyber Threat Intelligence Modeling for Connected Autonomous Vehicles

The advent of Connected Autonomous Vehicles (CAVs) has significantly transformed the landscape of intelligent transportation systems. However, the enhanced connectivity and complexity of these vehicles also introduce new cybersecurity vulnerabilities. In response to the escalating threats, the paper "Proactive Security Defense: Cyber Threat Intelligence Modeling for Connected Autonomous Vehicles" proposes the Actim framework, designed to advance proactive defense strategies through effective cyber threat intelligence (CTI) modeling.

This research centers on the development of an automotive CTI modeling framework capable of extracting and analyzing relationships among diverse cyber threat elements. The authors propose the vehicle security-safety conceptual ontology model that encapsulates various classes of threat entities and the relations among them. Essential components of the proposed framework include the manual annotation of an extensive CTI corpus derived from real-world cybersecurity data, comprising 908 threat intelligence texts, 8195 entities, and 4852 relationships.

To enhance the efficiency of CTI extraction, the paper introduces an advanced automotive CTI mining model leveraging a BERT-DocHiatt-BiLSTM-LSTM architecture based on a cross-sentence context. The model surpasses existing methodologies in performance, highlighting its potential in the accurate mining of complex descriptions within automotive cybersecurity data. The incorporation of this novel architecture allows for improved comprehension of entity relationships across document-level contexts, a significant advancement over traditional sentence-level extraction techniques.

One of the notable contributions is the creation of an automotive CTI knowledge graph, synthesized from the extracted relationships and entities. This knowledge graph fuses various cybersecurity elements, providing comprehensive insights into the evolving cyber threat landscape applicable to CAVs. The structured representation facilitates deeper analysis and understanding of the intricate web of interdependencies among cybersecurity threats and vehicle components.

The research surmounts several challenges inherent in CTI modeling for CAVs. Among these are the diversity of cybersecurity entities and relations, the complexity of CTI descriptions, and the scarcity of labeled CTI corpora. By addressing these challenges, the Actim framework not only advances the field of automotive cybersecurity but also sets a foundation for future research endeavors aimed at enhancing CTI modeling techniques.

Practical implications of this work are profound. The knowledge graph enables stakeholders to envisage the broader cyber threat landscape, potentially leading to more informed security policies and defense mechanisms. Theoretically, the integration of hierarchical attention mechanisms and cross-sentence context within the BERT-DocHiatt-BiLSTM-LSTM model enriches existing literature by demonstrating a method for capturing nuanced entity relationships at a document level.

Moving forward, advancements in the data augmentation techniques and additional corpus labeling could further bolster this framework's efficacy. Moreover, exploration into entity disambiguation and link prediction within the knowledge graph holds promise for even deeper operational insights. Overall, this paper sets a pivotal precedent in proactive security defense within the domain of connected autonomous vehicles through sophisticated CTI modeling frameworks.