The growing demands for high-precision sensing and ultra-reliable low-latency communications in Internet of Vehicles (IoV) networks, coupled with increasingly congested spectrum resources, have driven integrated sensing and communication (ISAC) technologies toward millimeter-wave (mmWave) frequency bands. However, apart from the inherent openness of wireless channels, this transition exposes critical security gaps that the conventional physical layer security methods featured by communication interference struggle to mitigate: the cross-domain coupling interference between communication and sensing subsystems among vehicles, forming an emergent threat landscape in ISAC-IoV networks. Based on the fact that conventional forward-facing vehicular mmWave radars are typically equipped with horizontally oriented narrow beam, and have limitations in vertical resolution due to the utilization of 1D horizontally arranged antenna arrays, in this paper, we propose a 3D communication-2D sensing (Com3DSen2D) enabled spatial separation and interference decoupling framework. Furthermore, under the proposed Com3DSen2Dframework, to make a balance between sensing accuracy, communication reliability and security, we formulate a non-convex optimization problem of secrecy rate maximization through jointly optimizing 3D-BF design of communication subsystems and radar sensing power allocation of sensing subsystems, subject to the expected levels of sensing accuracy and communication reliability constraints. Experimental evaluations demonstrate that our joint optimization approach achieves significant performance gains over individual optimization benchmarks, yielding 25.3% and 57.8% improvements in secrecy rate compared to isolated 3D-BF optimization and radar sensing power allocation, respectively. Moreover, experimental results obtained from the hardware platform further validate the effectiveness of our proposed framework. This work establishes a comprehensive solution for coupling interference management and security enhancement in next-generation ISAC-IoV networks.

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real-time optimization and acritical temporal mismatch between slow mechanical movement and rapid channel variations. Although data-driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning-based approaches often overlook communication-specific domain knowledge—particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer-based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user-specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3606 bps/Hz and a Secrecy Performance Coverage Probability (SPSC) of 79.26%,outperforming the state-of-the-art predictive baseline by 35.5% and 6.73 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security methods, which depends on the additional communication interference costing extra power resources, in this paper, we investigate a novel physical layer security solution, under which the inherent radar sensing interference of the vehicles is utilized to secure wireless communications. To measure the performance of physical layer security methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that the sensing interference utilization, combined with joint design of transmission power and straight trajectory of the transmitter, achieves a secrecy rate of 3.92bps/Hz for different noise powers for the case of perfect channel state information (CSI). The proposed method maintains robustness, achieving a 60.17% improvement of TRSA_SR under unavailable CSI and location information of the Eve.