Device authentication protocols based on a strong physical unclonable function (PUF) show promise for enhancing Internet of Things security. However, a strong PUF is vulnerable to machine learning (ML) attacks. This paper proposes a Feistel structure sequential obfuscation-based PUF (Feistel-PUF) to resist ML attacks. When the PUF is obfuscated by the Feistel structure, the challenge-response relationship resembles that of sequential logic circuits. Specifically, each response depends on both current and historical challenges, thereby significantly increasing the complexity of the challenge-response mapping. Experimental results showed that even with 1 million collected challenge response pairs, the prediction accuracies of ML attacks on Feistel-PUF remained at approximately 50%. Notably, the obfuscation process excluded response data, thereby preserving the randomness, reliability, and uniqueness of PUF with negligible performance degradation. The Feistel structure was iteratively reused for sequential obfuscation processing, maintaining constant and minimal hardware overhead. We propose a novel approach that combines the Feistel-PUF with an authentication protocol. This system synchronizes obfuscation parameters during the initial registration phase and implements dynamic updates throughout the authentication process. This approach allows the obfuscation structure to be made public, thereby overcoming the reliance on structure or algorithm secrecy in traditional dynamic challenge obfuscation schemes. The security of the protocol was confirmed by subjecting it to ProVerif verification and security analysis.

Addressing security threats faced by high efficiency video coding (HEVC) video devices in open deployment environments, this article proposes a HEVC Transform and Quantization (TQ)-based Software Physical Unclonable Function (TQ-SPUF). By overclocking the HEVC TQ module, this function induces clock violations on the critical path, thereby generating a stable and unique device fingerprint. Meanwhile, a two-stage postprocessing scheme combining majority voting and butterfly-xoroperations is employed to generate stable and uniform device fingerprints. Based on the proposed TQ-SPUF, a lightweight challenge-response authentication protocol was designed to achieve device identity binding. Furthermore, the PUF-gated session key is utilized to drive a dual-perturbation selective encryption scheme, which introduces both fixed- and random-position perturbations into the I-frame network abstraction layer units. In this way, semantic information exploitable was effectively disrupted, thereby preventing content recovery or tampering. Experimental results show that the proposed TQ-SPUF achieves 98.84% randomness and 49.15% uniqueness, passing part of the NIST test. Moreover, the encrypted videos exhibit an average peak signal-to-noise ratio (PSNR) of 10.4313dB and an average structural similarity index measure (SSIM) of 0.2935. This indicates that the encrypted content is visually incomprehensible, thereby achieving video anti-tampering protection.

In the era of intelligent computing, with the aid of Internet of Things (IoT) technology, artificial intelligence (AI) chips can be embedded at the terminal, object, edge, and cloud levels, ultimately achieving the vision where there is computation, there is AI intelligence. This not only enhances the efficiency of production and daily life but also exponentially increases the risk of privacy information leakage within AI models. This article leverages the characteristics of strong physical unclonable functions (PUFs), in which the inherent feature information is hidden in physical variations and difficult to steal, to design a security protocol based on strong PUFs that provides effective protection for AI model parameters in the IoT environment. The protocol treats AI model parameters as responses and selects challenges capable of generating these responses. Since the responses generated by the challenges can be considered as randomly generated, transmitting the challenges does not disclose the response information, thus avoiding the risk of parameter hacking. Additionally, the protocol utilizes machine-learning modeling techniques and lightweight encryption technologies to reduce the storage costs for identity information and the computational overhead of encryption operations. Through a security analysis of the protocol, it demonstrates that even under ideal attack conditions, the proposed protocol can resist various attacks. By using formal verification with the ProVerif tool, it confirms the security of the protocol flow and the effective protection of private information.

With the increasing number of devices in the Internet of Things (IoT), security has become a necessary feature. Compared to traditional key encryption methods, IoT device authentication protocols based on strong Physically unclonable function (PUF) have the advantage of being difficult to leak and tamper with. In addition, the protocol can enhance the resistance of strong PUFs to machine learning (ML) attacks by encrypting private information. However, the quality of the authentication is strongly related to the stability of the generated responses. If a large cost is incurred to improve the stability of the strong PUF, it will consume the already scarce resources of the device. This article proposes a challenge screening strategy to improve the response stability of a commonly used strong PUF, arbiter PUF (APUF). First, a precise modeling method of APUF is carried out using the logistic-regression ML algorithm. Subsequently, random noise is injected into the challenges or PUF mathematical model and then calculated to estimate whether a stable response can be produced. Finally, stable challenges are used for IoT device authentication, while the threshold of the protocol is increased in parallel. Experimental verification shows that the proposed strategy can increase the stability of APUF responses to 100% at different temperatures. Furthermore, it does not consume hardware overhead at the device end, which is of particular significance for applications in resource-constrained conditions.

Unmanned Aerial Vehicle (UAV) swarms networks have gained increasing significance in daily life and work. However, current UAV authentication protocols face critical security challenges such as high computational overhead, complex key management, and vulnerabilities to network attacks. This paper proposes a hierarchical authentication protocol that enhances resilience against attacks while maintaining ultra-low overhead. The protocol employs a two-tier mutual authentication architecture, comprising authentication between the base station and the server, and between the server and the UAV leader. This design effectively reduces the risk of single-point failure and improves scalability in large-scale swarm scenarios. Physical unclonable function (PUF) technology establishes secure UAV identities, combined with randomized leader election enhance security, reduce overhead, and increase attacker localization complexity. This paper presents a lightweight cryptographic strategy that combines hash-based challenge encryption and XOR-based response obfuscation to disrupt predictable PUF challenge-response mappings, countering machine learning (ML)-based modeling attacks. The proposed protocol passes security tests using ProVerif and Scyther. The scheme achieves 58.21% lower communication costs than existing approaches, with an authentication overhead of 58.04 μs.

The Internet of Things (IoT) plays an important role in all aspects of production and day-to-day life. However, owing to the frequently trusted authentication vulnerabilities, the physical unclonable function (PUF) has unique advantages in the field of equipment authentication because of its nonstorage and nonvolatility. Nevertheless, PUFs are vulnerable to machine learning (ML) attacks. Once a model is constructed accurately, the secrecy of the PUF is lost. Therefore, Bagua matrices are proposed in this study, which can greatly reduce the accuracy of modeling by encrypting the challenge information. On this basis, a whole-process configurable IoT sensing device protocol was constructed for authentication and transmission, and different matrix encryption methods were configured according to the needs of the different devices. Moreover, on the premise of trusted authentication, the perceptual information can be encrypted using a preset matrix. According to the implementation results of the scheme, the resistance to the ML attacks of the PUF improved significantly and the device authentication and encrypted transmission could operate normally.