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.

One of the features of network traffic in Internet of Things (IoT) environments is that various IoT devices periodically communicate with their vendor services by sending and receiving packets with unique characteristics through private protocols. This paper investigates semantic attacks in IoT environments. An IoT semantic attack is active, covert, and more dangerous in comparison with traditional semantic attacks. A compromised IoT server actively establishes and maintains a communication channel with its device, and covertly injects fingerprints into the communicated packets. Most importantly, this server not only de-anonymizes other IPs, but also infers the machine states of other devices (IPs). Traditional traffic anonymization techniques, e.g., Crypto-PAn and Multi-View, either cannot ensure data utility or is vulnerable to semantic attacks. To address this problem, this paper proposes a prefix- and distribution-preserving traffic anonymization method named PD-PAn, which generates multiple anonymized views of the original traffic log to defend against semantic attacks. The prefix relationship is preserved in the real view to ensure data utility, while the IP distribution characteristic is preserved in all the views to ensure privacy. Intensive experiments verify the vulnerability of the state-of-the-art techniques and effectiveness of PD-PAn.

The Internet of Vehicles (IoV) has witnessed a substantial surge in its expansion during recent years, with the integration of edge servers emerging as an increasingly prevalent practice to cater to content requests that demand a superior quality of service. Nonetheless, conventional cache policies often prove inadequate for IoV applications, primarily due to their incapacity to effectively handle a diverse array of content requests, the high-speed mobility of vehicles, and the inherent instability of network connections. Within the scope of this study, we commence by bifurcating various content requests into two distinct categories: latency-sensitive content request and bandwidth-sensitive content request. Subsequently, we establish a model to evaluate the service quality under the joint consideration of different types of content requests, the mobility of vehicles and storage capacity of edge nodes. Furthermore, we transform the quality-of-service (QoS) penalty function into a system reward function. This adaption enables us to propose an innovative edge cache scheme founded upon the Deep Deterministic Policy Gradient (DDPG) algorithm of Reinforcement Learning (RL) method, which empowers dynamic adjustments in response to the evolving IoV environment. To validate the effectiveness of our proposed approach, we harness the Simulation of Urban Mobility (SUMO) traffic simulation software and construct a traffic road scenario based on a specific part segment of Nanjing Beltway. A wide-ranging set of contrast experiments was performed to ensure the improved performance of the DDPG based deep reinforcement learning method. Simulation experimental results show that the proposed algorithm converges quickly and outperforms existing algorithms in terms of service quality-hit ratio for latency-sensitive content and transmission speed for bandwidth-sensitive content.

Anonymous networks facilitate privacy-preserving information transmission by ensuring data confidentiality and communication anonymity. While encryption effectively secures content, anonymity remains vulnerable to traffic analysis, which can expose users' network identities and communication relationships. Consequently, anonymity alone is insufficient, necessitating integrated technologies to further strengthen communication security. This paper introduces covert communication to anonymous networks by proposing a novel Covert Information Transmission Framework over Anonymous Networks, designed to realize both covert and anonymous data transfer. To the best of our knowledge, this is the first work to propose covert communication schemes specifically within anonymous networks. In our framework, we establish covert channels in the Tor network and transmit covert information based on Tor's hidden service protocol. Utilizing Tor's anonymous circuits, we strategically select suitable protocol data fields as information carriers and propose three covert communication schemes with distinct characteristics. These schemes enable secure data exchanges between clients and hidden services, catering to diverse communication requirements. Finally, we implement the proposed framework in a real-world Tor network and conduct extensive experiments to evaluate its feasibility and performance.

In AIoT‐driven applications such as precision healthcare, multiple devices communicate through continuous sharing of fragmented and sovereignty‐constrained data. This poses critical challenges in enabling secure data sharing among multiple trustless stakeholders under fine‐grained access control policies. Although blockchain provides a user‐sovereign and trust‐enhancing framework, existing work remains inefficient in healthcare scenarios with frequent recipient and data changes due to repeated operations. It requires re‐authorizing every authorized segment for each recipient and relies on non‐batchable mechanisms for transaction atomicity, resulting in additional computational cost and on‐chain transaction overhead. To address these limitations, we propose the Dynamic Authorization with Batched Transaction Atomicity (DABTA) protocol that achieves dynamic authorization with fixed overhead and batched data sharing transaction atomicity. DABTA introduces a constant‐size combination‐bound artifact to decouple authorized recipients from authorized content, eliminating repeated encryption when either the shared data or the recipient set changes. Furthermore, DABTA aggregates multiple authorization transactions under a shared commit checkpoint and performs batched decryption to recover transaction payloads, thereby amortizing the overhead of ensuring transaction atomicity across concurrent authorizations. We prove the correctness and security of DABTA. A prototype built on Hyperledger Fabric achieves 97.4 authorizations per second, delivering approximately 68.2% higher throughput than a non‐batched baseline.