On vehicular named data network (VNDN), Interest Flooding Attack (IFA) can exhaust the computing resources by sending a large number of malicious Interest packets, which leads to the failure of satisfying the legitimate requests and seriously hazards the operation of Internet of Vehicles (IoV). To solve this problem, this paper proposes a distributed network traffic monitoring-enabled multi-indicator detection and prevention approach for VNDN to detect and resist the IFA attacks. In order for facilitating this approach, a distributed network traffic monitoring layer based on road side unit (RSU) is constructed. With such a monitoring layer, a multi-indicator detection approach is designed, which consists of three indicators: information entropy, self-similarity, and singularity, whereby the thresholds are tweaked by the real-time density of traffic flow. Apart from the detection, a blacklisting based prevention approach is realized to mitigate the attack impact.We validate the proposed approach via prototyping it on our VNDN experimental platform using realistic parameters setting and leveraging the original NDN packet structure to corroborate the usage of the required Source ID for identifying the source of the Interest packet, which consolidates the practicability of the approach. The experimental results show that our multi-indicator detection approach has a greatly higher detection performance than those of using indicators individually, and the blacklisting-based prevention can effectively mitigate the attack impact as well.

With the popularity of the global positioning system (GPS) and mobile Internet, a large amount of vehicle trajectory data has been generated and applied in intelligent transportation systems. The collected trajectory data often contains sensitive user information, which poses a risk of user privacy disclosure. To enhance the privacy of vehicle trajectory data, this article proposes a novel two-layer privacy preserving (TLPP) mechanism that leverages clustering features. Initially, density-based clustering is employed to derive regional attributes and density characteristics of trajectory points. Subsequently, a generative adversarial network (GAN) incorporating a long short-term memory (LSTM) network is utilized to learn the distribution of clustered trajectories, facilitating the generation of synthetic trajectories. These synthetic trajectories are then substituted for the original trajectories, constituting the first layer of privacy protection. To ensure the fidelity of the synthetic data, a novel generator loss function is designed, utilizing the Wasserstein distance to quantify the spatial similarity between the real and synthetic trajectories. Furthermore, to accommodate the personalized privacy requirements, a tailored differential privacy mechanism is introduced. This mechanism provides a second layer of privacy protection by introducing the region-specific perturbations to the data. The experimental results show that, compared with the other models, our approach can effectively protect the user privacy while ensuring the trajectory data utility.