AI-Empowered Lightweight In-Vehicle Network Security Mechanisms: From Cryptographic Algorithms to Collaborative Defense Architectures Guiyang Institute of Information Science and Technology, Gui

Format:

Book

Conference/Event

Author/Creator:

Zhou, You, author.

Contributor:

Ding, Kani

Yang, Guozhi

Zhang, Jigui

Conference Name:

2025 International Conference on Big Data, Internet of Things and Intelligent Transportation (BDIT2025) (2025-07-19 : Zhengzhou, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

With the rapid development of Internet of Vehicles (IoV) and cyber-physical systems (CPS), connected autonomous vehicles (CAVs) have also developed rapidly. However, at the same time, in-vehicle networks also face more security challenges, mainly in terms of resource constraints, dynamic attacks, protocol heterogeneity, and high real-time requirements. Firstly, the trade-offs between lightweight encryption primitives and their software and hardware collaborative design in terms of performance, resource overhead, and security strength are analyzed. Secondly, the resource efficiency of AI-based intrusion detection system (IDS) is evaluated at the edge. Finally, we propose a dynamic adaptive collaborative defense framework (DACDF), which integrates federated learning with dynamic weight distillation, blockchain authentication with lightweight verifiable delay function (Light-VDF) and cross-domain IDS with hierarchical attention feature fusion to deal with collaborative attacks in resource-constrained environments. At the same time, we also identify future research directions, including the migration path of quantum-resistant cryptography (PQC) and the application challenges of explainable AI (XAI) in security-critical authentication

Notes:

Vendor supplied data

Publisher Number:

2025-99-0132

Access Restriction:

Restricted for use by site license