Ultra-Wideband (UWB) technology has recently emerged as a transformative enabler of high-precision positioning systems. Despite its growing adoption across diverse applications, prior studies have claimed several successful distance deception attacks against UWB. To address heightened security concerns, companies like Apple introduce the ranging-awareness defense mechanism into their new version of the UWB interaction frameworks, which is proven to be effective against most known attacks. In this paper, we critically focus on the design flaws of state-of-the-art UWB interaction frameworks and propose Urey-ML, a novel machine learning-based UWB distance deception attack targeting UWB systems. To the best of our knowledge, this is the first attack capable of circumventing the defense mechanisms implemented in Apple’s UWB Nearby Interaction Framework (ANIF). Specifically, Urey-ML is built upon two critical breakthroughs. First, through network packet analysis, we discover that ANIF leaves a crucial message for key negotiation in an unprotected state. This vulnerability enables Urey-ML to bypass the encryption protection implemented by standard UWB systems. Second, to break the ranging-awareness defense, Urey-ML involves a reinforcement learning-based algorithm to optimize attack parameters. By leveraging this approach, Urey-ML can automatically and craftily generate attack signals that mimic the variations typically caused by normal human movement. Our experiments on commercial-off-the-shelf UWB products show that Urey-ML achieves centimeter-level UWB distance deception, with more than 25.79% signals circumventing the defense check of the victim device, which is only 0.56% (or failed) in prior works.

Drones are a viable solution for searching and rescuing people in distress at sea. However, traditional methods of unmanned aerial vehicle (UAV) search and rescue have slow emergency response speed, limited task environment perception and decision-making ability, high search and rescue task cost, and low efficiency. Therefore, this letter proposes a distributed drone dynamic ocean search and rescue framework that integrates deep reinforcement learning (DRL) and predicts the drift probability range based on ocean data. The existence probability of each area is calculated through a mixture of Gaussian distributions, and the possibility changes dynamically with ocean currents. The predicted probability will be input into the learning network in a matrix with the same dimension as the environment and trained using DDQN. In addition, we introduce a distance penalty in the reward function and establish a dynamic greedy strategy to accelerate learning and improve training accuracy and stability. This framework can dynamically identify the location of potential victims and guide drone groups to coordinate search and rescue operations. In particular, to adapt to the decision-making quality and synergy of DRL under distributed drone swarms, we introduce a distributed priority experience replay mechanism to share the best experiences of drones in different scenarios. By comparing various methods, this study shows excellent comprehensive performance and effectively improves search and rescue efficiency.