Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.

Integrated sensing and communication (ISAC) is envisioned as a key enabler of 6 G networks, allowing communication and sensing to share spectrum and hardware resources in a unified manner. Unmanned aerial vehicles (UAVs) can further enhance ISAC systems by providing flexible deployment and reliable line-of-sight (LoS) links. However, the openness of UAV communication channels, coupled with their dynamic mobility and limited energy, makes them highly vulnerable to eavesdropping and exacerbates physical layer security (PLS) concerns. To address these challenges, we propose a reconfigurable intelligent surface (RIS)-assisted framework for enhancing PLS in dynamic and uncertain UAV-enabled ISAC networks. We formulate a secrecy-oriented joint optimization problem that integrates UAV trajectory design, RIS phase shift configuration, beamforming, and decoupled uplink–downlink (DUDe) user association. To render this problem tractable, we adopt a two-stage approach. First, the beamforming subproblem is solved via fractional programming and successive convex approximation to obtain closed-form solutions. Subsequently, with beamforming fixed, we reformulate the remaining coupled design of trajectories, associations, and RIS phases as a Partially Observable Markov Decision Process (POMDP). To address the challenges of high-dimensional sequential decision-making and the need for scalable, real-time coordination inherent in multi-UAV systems, we develop a robust multi-agent deep reinforcement learning (MADRL) solution, effectively circumventing the prohibitive communication overhead of centralized control and the intractability of single-agent formulations suffering from the curse of dimensionality. Specifically, we design a stochastic environment-aware Proximal Policy Optimization (PPO) algorithm that incorporates random environment distribution to capture environmental randomness, intrinsic rewards to guide UAV exploration of secure regions, and uncertainty estimation to refine policy updates. Simulation results verify that the proposed framework significantly outperforms benchmark methods in terms of secrecy rate and robustness, especially under dynamic scenarios with varying numbers of users and eavesdroppers.

In Wi-Fi networks, devices can be identified by physical features or MAC layer features, and the solutions of device identification can be used to enhance device authentication. Since 802.11ac Standard has been widely applied in Wi-Fi devices in recent years, the traditional identification methods designed for 802.11b/g/n devices will be no longer applicable. Therefore, it is necessary to design the corresponding 802.11ac device identification method. Compared with the physical feature-based method, the MAC layer-based method has advantages of low cost and easy deployment, so it has attracted more and more researchers' attention. In this paper, we use the fields from 802.11ac MAC frame as fingerprints. Through the analysis of 802.11ac MAC frame, a preprocessing method of the frame is proposed to mask strong and easy-to-modified identifiers. Then to overcome the difficulties caused by random changes in field values, we propose a device identification method based on the deep learning to select features automatically. Compared with the previous one using the transmitting rate as a feature, our method does not spend much time capturing packets in the device identification stage and has better performance whose average precision and recall exceed 99%.