Cyber-Physical Systems (CPS) consist of various physical devices and have a significant impact on both industry and the economy. In CPS, controllers or control centers typically send continuous commands to actuators. Due to the limited computational and storage resources of CPS devices, existing Message Authentication Code and digital signature algorithms face challenges in terms of being non-interactive, lightweight, and continuous on the actuator side. To address this issue, we propose a lightweight continuous message authentication (LCMA) scheme that ensures the continuity of signatures through a mechanism involving random numbers and incrementing sub-private keys. The multidimensional Bloom filters is designed and used togather with precomputed verification points generated by Key Generation Center to enable non-interactive verification. Additionally, we design a redundancy-free hash tree for CPS to reduce information redundancy. The proposed solution requires only hash operations and Bloom filter queries for verification, achieving sufficient lightweight performance. Finally, we provide a formal security proof for the proposed scheme and simulate it on the ESP32 platform. The results show that the implementation times for signature generation and verification are 0.779 milliseconds and 0.792 milliseconds, respectively. Compared to other non-interactive lightweight message authentication schemes, the proposed LCMA is more suitable for CPS devices.

Deep Learning (DL)-based weakly supervised anomaly detection methods enhance the security and performance of communication and networks by promptly identifying and addressing anomalies within imbalanced samples, thus ensuring reliable communication and smooth network operations. However, existing DL-based methods often overly emphasize the local feature representations of samples, thereby neglecting the long-range dependencies and the prior knowledge of the samples, which imposes potential limitations on anomaly detection with a limited number of abnormal samples. To mitigate these challenges, we propose a Transformer deviation network for weakly supervised anomaly detection, called TFD-Net, which can effectively leverage the interdependencies and data priors of samples, yielding enhanced anomaly detection performance. Specifically, we first use a Transformer-based feature extraction module that proficiently captures the dependencies of global features in the samples. Subsequently, TFD-Net employs an anomaly score generation module to obtain corresponding anomaly scores. Finally, we introduce an innovative loss function for TFD-Net, named Transformer Deviation Loss Function (TFD-Loss), which can adequately incorporate prior knowledge of samples into the network training process, addressing the issue of imbalanced samples, and thereby enhancing the detection efficiency. Experimental results on public benchmark datasets demonstrate that TFD-Net substantially outperforms other DL-based methods in weakly supervised anomaly detection task.

Deep unrolling architectures have revitalized compressive sensing (CS) by seamlessly blending deep neural networks with traditional optimization-based reconstruction algorithms. In pursuit of an efficient and deep interpretable approach, we propose LTwIST for CS problem, a novel deep unrolling framework that draws inspiration from the well-known two-step iterative shrinkage thresholding (TwIST) algorithm. LTwIST uses a trainable sensing matrix to adaptively learn structural information in images, and introduces a customized U-block architecture to solve the proximal mapping of nonlinear transformations connected with the sparsity-inducing regularizer. Specifically, each iteration recovery step of LTwIST corresponds to an iterative update step of the traditional TwIST algorithm. Moreover, the proposed method is designed to learn all the parameters end-to-end without manual tuning such as shrinkable thresholds, step sizes, etc. As a result, LTwIST obviates the need for manual parameter optimization, allows for high-quality image recovery and provides unambiguous interpretability. Moreover, our proposed LTwIST is also applicable to CS-based magnetic resonance imaging and exhibits a strong reconstruction performance. Extensive experiments on several public benchmark datasets demonstrate that the proposed LTwIST outperforms existing state-of-the-art deep CS methods by considerable margins in terms of quality evaluation metrics and visual performance. Our code is available on LTwIST.