With the rapid advancement of mobile communication technologies, privacy preservation in VANETs has emerged as a pivotal research frontier. Previous authentication protocols often face challenges such as key leakage risks and high computational overhead. Physical Unclonable Functions (PUFs), as a lightweight hardware primitive, offer a promising solution for enhancing security in VANETs. Recently, Xie et al. designed an anonymous authentication protocol for VANETs. Unfortunately, our analysis reveals that their protocol cannot resist ephemeral key leakage attacks. To address these limitations, we propose an enhanced PUF-based anonymous authentication protocol, referred to as iXDZ. Our protocol leverages PUF challenge-response mechanisms to generate real-time vehicle keys, eliminating the need to store long-term keys on vehicles and thereby preventing physical key extraction attacks. Our protocol not only resists ephemeral key leakage attacks but also utilizes the uniqueness, unpredictability, and tamper-resistance of PUF to defend against RSU capture attacks and various physical attacks, meeting the diverse security requirements of VANETs. Furthermore, it is anonymous and lightweight, protecting user privacy and enhancing overall network trust. We rigorously demonstrate the security of iXDZ under the random oracle model and supplement the proof utilizing the Scyther formal analysis tool. Performance evaluations reveal that iXDZ significantly enhances security while reducing computational and communication overhead. Additionally, a case study highlights that iXDZ achieves a 33.3% reduction in execution time compared to the original protocol.

In recent years, the security of Dilithium against side-channel attacks (SCA) has attracted great attentions from the cryptographic engineering community. However, existing power analysis attacks cannot fully utilize the side-channel leakages of the Dilithium reference implementation to efficiently recover the private key. In light of this, a screening least square technique assisted multivariate template attack (SLST assisted MTA) is proposed in this paper. In SLST assisted MTA, side-channel leakages of coefficient$y_{i}$of random polynomial y, unsigned number$x_{i}$and random byte string$a_{i^{\prime }}$can be utilized simultaneously to recover coefficient$y_{i}$of random polynomial y with MTA. Then, one can build error-tolerant equations, and the private key$\mathbf {s_{1}}$can be solved with SLST efficiently. We evaluate the private key recovery efficiency of SLST assisted MTA with real traces measured from the Cortex-M4 processor based Dilithium reference implementation, and the evaluation results show that with MTA, 19.41%, 15.70% and 16.88% of the coefficients of y can be accurately recovered in cases of Dilithium 2, 3 and 5. Besides, using SLST, after five times screening, only 38, 40 and 39 power traces are enough to recover private key$\mathbf {s_{1}}$of Dilithium 2, 3 and 5 with 100% of success rate.

Due to the limitations of the existing annotation methods, the prevalence of label noise can be caused in realistic malicious traffic datasets, which has a significant impact on the training and evaluation of deep learning-based intrusion detection models. Recently, various methods have been proposed to deal with noise-containing labeled datasets, and they can be roughly divided into two categories: data cleaning and robust training. However, the different processing ideas lead these two types of methods to ignore the information in different components of the dataset, resulting in a cliff-like drop in performance under high noise conditions. To this end, this study proposes a unified framework for handling noise malicious traffic based on the multidimensional constrained representations named MCRe, which unifies data cleaning and robust training into an ideal representation function approximation. According to the properties of the ideal representation function, information integrity constraints, cluster separability constraints and core proximity constraints are defined to drive MCRe to approximate the ideal representation during iteration. These constraints led MCRe to learn the individual, intra-class, and global levels of distributed knowledge, thus avoiding irrational domain knowledge extraction and ensuring strong label noise robustness of the representation network. We validated MCRe on a dataset that includes 22 types of realistic malicious traffic. Experimental results show that MCRe can outperform the state-of-the-art methods in both data cleaning and robust training downstream tasks, achieving 85% pure sample rate and 82% classification accuracy even under the condition of up to 90% noise labels. In addition, the generalizability of MCRe was verified on several public datasets. Finally, MCRe was also well-extended to enhance other data cleaning and robust training approaches.