부산대학교 도서관

The automotive industry is undoubtedly taking giant strides toward a paradigm shift. In essence, wireless network communication and artificial intelligence technologies are stimulating the gradual evolution of the autonomy of intelligent vehicles. This shift causes a divergence in vehicle architecture to become assembled with software-driven rather than mechanical-driven components, producing an integrated connected central unit that perceives and processes the surrounding environment, makes autonomous decisions, and controls the entire vehicle. The emerging vehicular network technologies, including vehicle-to-everything and in-vehicle channels, facilitate wired and wireless bidirectional communication within the vehicle and to other vehicles, infrastructure actors, and the Cloud to integrate it with its surrounding environment in real-time. Despite the promised potential benefits of intelligent vehicles, including improved mobility, driving safety, and economic and environmental gains, such increased network connectivity and complexity expose them to a vast attack surface. Researchers have identified a wide range of internal and external security threats due to connectivity and automation vulnerabilities within the vehicle-to-everything wireless network channels and the lack of core security measures, such as authentication, authorization, and encryption within the in-vehicle network. The dynamic nature of these vehicular networks and their ever-changing threat landscape originate new pressing security challenges that can cause severe safety destruction. In this paper, we propose BoostSec, a novel online security analytics solution that leverages advanced incremental ensemble learning to provide robust, rapid, and adaptive protection of vehicular networks against known and unknown attacks. We further augment the proposed solution with an agnostic interpretability analysis of the results. We conducted extensive experiments on three publicly available benchmark datasets representing vehicular environments in various contexts. The experimental evaluation proves that the proposed framework outpaces current baseline approaches and meets the challenges with remarkable performance, demonstrated by its (1) generalization covering a wide range of attacks across various vehicular network contexts; (2) real-time analysis reflected in efficient computation footprint; (3) adaptability against unseen attacks; (4) robustness against adversarial attacks; and (5) augmented interpretability analysis. [ABSTRACT FROM AUTHOR]