Strong Physical Unclonable Functions (PUFs) are considered the most promising circuits for lightweight IoT authentication. However, current white-box attacks on PUF circuits through reverse engineering compromise the effectiveness of many anti-modeling attack techniques. To address this challenge, we propose a configurable four-state Hybrid PUF (CF PUF), which includes three strong PUFs and one weak PUF. The proposed structure uses the key stream generated in the weak PUF state to control the configuration state transition sequence of the CF PUF. This approach allows the PUF’s challenge-response mapping structure to be dynamically controlled by the unpredictable weak PUF, rather than relying on a fixed PUF structure, thereby providing enhanced resistance to white-box attacks through reverse engineering. To hide the weak PUF characteristics and prevent reverse analysis attackers from using divide-and-conquer methods to break the weak PUF, we employ LUT6_2 resources to embed the weak PUF structure into strong PUFs with minimal hardware overhead. The proposed CF PUF was implemented and evaluated on an Xilinx Artix-7 FPGA hardware platform. Experimental results demonstrate that the proposed solution achieves good prediction resistance, with the best modeling accuracy limited to 54.6% under 1M CRPs across Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) attacks. Moreover, it maintains good reliability, averaging 98.02% under temperature variations from -20 °C to 80 °C.

Next-generation wireless communication systems are unifying massive MIMO and integrated sensing and communication (ISAC) to enhance sensing and communication performance simultaneously. In this paper, the signal detection problem for MIMO-ISAC systems is modeled as a mixed-integer least squares problem (MILSP). To solve it in an efficient way, an iterative algorithm combining alternating direction method of multipliers (ADMM) and neighborhood search (NS) technique is proposed, which is named as NS-ADMM. Specially, at each iteration, the output of ADMM serves for the following neighborhood search to achieve the extra performance gain. Moreover, a flexible mechanism of ADMM iterations is also given for a better estimation of the sensing signals. Finally, simulations demonstrate the proposed NS-ADMM algorithm has significant performance advantages with low computational complexity.

This article addresses the challenge of accurate target localization in fifth-generation (5G) communication networks using multistatic multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) waveforms. Conventional on-grid compressed sensing-based target parameter estimation methods degrade significantly when targets are located off the predefined grid points. To overcome this limitation, we propose an off-grid compressed sensing approach that uses a grid evolution technique specifically designed for the complex-valued, block sparse structure inherent in multistatic MIMO-OFDM signal. By adaptively refining the grid during the sensing process, the proposed method achieves improved target localization accuracy, particularly in off-grid scenarios. Simulation results demonstrate that this approach significantly outperforms traditional methods, enhancing localization accuracy for 5G-enabled sensor networks.

Simple authentication protocols based on conventional physical unclonable functions (PUFs) are vulnerable to modeling attacks and other security threats. This article proposes an arbiter PUF based on a linear feedback shift register (LFSR-APUF). Different from the previously reported linear feedback shift register (LFSR) for challenge extension, the proposed scheme feeds the external random challenges into the LFSR module to obfuscate the linear mapping relationship between the challenge and response. It can prevent attackers from obtaining valid challenge–response pairs (CRPs), increasing its resistance to modeling attacks significantly. A 64-stage LFSR-APUF has been implemented on a field programmable gate array (FPGA) board. The experimental results reveal that the proposed design can effectively resist various modeling attacks, such as logistic regression (LR), evolutionary strategy (ES), artificial neuro network (ANN), and support vector machine (SVM) with a prediction rate of 51.79% and a slight effect on the randomness, reliability, and uniqueness. Further, a lightweight authentication protocol is established based on the proposed LFSR-APUF. The protocol incorporates a low-overhead, ultralightweight, novel private bit conversion Cover function that is uniquely bound to each device in the authentication network. The proposed authentication protocol not only resists spoofing attacks, physical attacks, and modeling attacks effectively but also ensures the security of the entire authentication network by transferring important information in encrypted form from the server to the database even when the attacker completely controls the server.

Abstract In passive radar and joint communication and radar sensing (JCRS), target sensing with the multiuser multiple input multiple output orthogonal frequency division multiplexing (MU‐MIMO‐OFDM) modulation signal is gaining increasing interest. Multiple transmit nodes emitting the MU‐MIMO‐OFDM modulation signals at the same carrier frequency and one receiver collecting the target‐reflected signals for target location and velocity estimation are considered. This is a typical scenario when using the fifth‐generation (5G) communication network signal for target sensing. In this scenario, the echo signals corresponding to different transmit nodes are not resolved in the receiver, and the modulated data symbols cannot be removed from the received signals. Most traditional parameter estimation methods in passive radar and JCRS may not be suitable here. A location and velocity estimation method with the received echo signals is proposed. Specifically, the location parameters are extracted directly from the received echo signals. The location estimation is cast into a block sparse vector reconstruction problem. The variational Bayesian sparsity learning (VBSL) method is exploited for the reconstruction of the block sparse vector. Accelerated VBSL methods are developed for improving the computational efficiency. Simulations verify the effectiveness of the proposed methods.

Strong PUF represented by arbiter PUF is suitable for the authentication of resource-constrained devices. However, conventional arbiter PUF is vulnerable to modeling attacks due to its linear structure. In this paper, we propose an arbiter PUF with time-variant obfuscation scheme (TVO-APUF), which feeds the external random challenges into the linear feedback shift register (LFSR) module to determine the real challenge of underlying arbiter PUF, thus obfuscating the linear mapping relationship between challenge and response, leading to significant resistance to modeling attacks. In addition, LFSR module with low hardware overhead can be updated at any time to prevent reply attack. We implement a 48-stage TVO-APUF on Xilinx Spartan-6 FPGA board. The experimental results show that the proposed TVO-APUF can effectively resist modeling attacks such as logistic regression (LR), support vector machine (SVM) and evolutionary strategy (ES) with a maximum prediction rate of 53 % and slight effects on uniformity, stability and uniqueness.