Unmanned Underwater Vehicles (UUVs) play an irreplaceable role in marine exploration, environmental monitoring, and national defense. The UUV depends on underwater acoustic communication (UAC) technology to enable reliable data transmission and support efficient collaboration. As the complexity of UUV missions has increased, secure UAC has become a critical element in ensuring successful mission execution. However, underwater channels are inherently characterized by high error rates, limited bandwidth, and signal interference. These problems severely limit the efficacy of traditional security methods and expose UUVs to the risk of data theft and signaling attacks. Cryptography-based security methods are important means to protect data, effectively balancing security requirements and resource constraints. They provide technical support for UUVs to build secure communication. This paper systematically reviews key advances in cryptography-based secure UAC technologies, focusing on three main areas: (1) efficient authentication protocols, (2) lightweight cryptographic algorithms, and (3) fast cryptographic synchronization algorithms. By comparing the performance boundaries and application scenarios of various technologies, we discuss the current challenges and critical issues in underwater secure communication. Finally, we explore future research directions, aiming to provide theoretical references and technical insights for the further development of secure UAC technologies for UUVs.

In the current 5G vehicle network system, there are security issues such as wireless intrusion, privacy leakage, and remote control. To address these challenges, an improved lightweight anonymous authentication key negotiation scheme based on certificate-less aggregate signatures is proposed and its security and efficiency are analyzed. The result shows that the scheme can offer security attributes including anonymity, traceability, and revocability, as well as effective identity authentication, and it can resist forgery attacks, man-in-the-middle attacks, tampering attacks, and smart card loss attacks. Moreover, compared with similar schemes, it possesses superior security and more efficient computational efficiency and less communication overhead, thereby being more appropriate for high-speed, large-capacity, low-latency, and resource-constrained 5G vehicle network application scenarios.

Unmanned aerial vehicle mobile ad hoc networks (UAVMANETs) formed by multi-UAV self-assembling networks have rapidly developed and been widely used in many industries in recent years. However, UAVMANETs suffer from the problems of complicated key negotiations and the difficult authentication of members’ identities during key negotiations. To address these problems, this paper simplifies the authentication process by introducing a Latin square to improve the process of signature aggregation in the Boneh–Lynn–Shacham (BLS) signature scheme and to aggregate the keys negotiated via the elliptic-curve Diffie–Hellman (ECDH) protocol into new keys. As shown through security analysis and simulations, this scheme improves the efficiency of UAVMANET authentication and key negotiation while satisfying security requirements.

Pseudo-random number generators (PRNGs) are essential for cryptography, secure communications, and simulations. Traditional chaos-based PRNGs suffer from periodicity degradation, inadequate statistical properties, and security vulnerabilities. To address these limitations, this paper proposes a novel PRNG model based on a generative adversarial network (GAN) integrating BiLSTM and Transformer architectures, termed BiLSTM-Transformer-GAN. A hybrid training set constructed from multiple chaotic systems provides rich samples. Within the generator, BiLSTM captures short-term dependencies, a lightweight sparse Transformer enhances long-range features, and chaotic positional encoding improves sensitivity to initial conditions. The discriminator employs parallel multi-branch dilated convolutions to capture multi-scale temporal features. The model adopts the Wasserstein GAN with gradient penalty (WGAN-GP) framework to ensure statistical matching with real chaotic sequences while avoiding mode collapse. Experimental results show that the generated sequences pass all NIST SP 800-22 and TestU01 tests, achieving an information entropy of 7.98 and a Lyapunov exponent of 10.23. Furthermore, by embedding chaotic signals during training, the generator produces sequences in a single forward pass at inference, significantly reducing per-step computational cost compared with traditional iterative chaotic PRNGs. These results validate the model’s superior randomness, chaotic properties, computational efficiency, and cryptographic security, offering an innovative approach for high-security PRNG applications.