<p>This paper presents a distributed event-triggered adaptive platooning control strategy to mitigate the effects of deception attacks on connected vehicles. A novel event-triggering scheme, which exploits both system states and parameter estimation signals, is proposed to efficiently conserve communication resources in vehicle-to-vehicle networks. Since data transmission occurs only at triggering instants, attack signals may overlap with normal transmissions, posing risks of degraded tracking performance or even inter-vehicle collisions. To address this issue, a radial basis function neural network is applied to estimate unknown attack signals during these triggering instants, with an adaptive controller synthesized to compensate for their adverse effects. By means of Lyapunov stability analysis, we rigorously prove that all platoon tracking errors converge to a small neighborhood of the origin. Furthermore, a joint design approach for control gains and event-triggered parameters is presented, and the effectiveness of the proposed strategy is verified via numerical simulations on a connected vehicle platoon.</p>