In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.

Industrial control systems (ICSs), as critical national infrastructures, are increasingly susceptible to sophisticated security threats. To address this challenge, our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring in ICS. The CAE-T utilizes unsupervised deep learning, employing a convolutional autoencoder for spatial feature extraction from multidimensional time-series data, and combines this with a transformer architecture to capture long-term temporal dependencies. The design of the model facilitates rapid training and inference, while its dual-component approach, utilizing an optimization function based on support vector data description (SVDD), enhances detection accuracy. This integration synergistically combines spatiotemporal feature extraction, significantly improving the robustness and precision of anomaly detection in ICS environments. The CAE-T model demonstrated notable performance enhancements across three industrial control system datasets. Notably, the CAE-T model achieved approximately a 70.8 increase in F1 score and a 9.2 rise in AUC on the WADI dataset. On the SWaT dataset, the model showed improvements of approximately 2.8 in F1 score and 5 in AUC. The power system dataset saw more modest gains, with an approximately 0.1 uptick in F1 score and a 1 increase in AUC. These improvements validate the CAE-T models efficacy and robustness in anomaly detection across various scenarios.

With the continuous advancement of automotive intelligence and ubiquitous connection, In-Vehicle Networks (IVN) are confronted with heightened security challenges. Attackers can inject false messages to manipulate or disrupt critical functional modules on the vehicle, leading to significant safety issues. Given the real-time requirements and resource constraints of the in-vehicle network, a lightweight Intrusion Detection System (IDS) is needed to deal with the security threats caused by attacks. This paper proposes an effective intrusion detection method to address these requirements. The method combines refined CAN traffic features with a lightweight Deep Learning (DL) network. The time interval series, ID and the CAN message payload are extracted by a T-shaped window, vectorized to form a one-dimensional data frame, and the detection is completed by a lightweight 1D deep learning model using efficient convolution calculation. In binary and multiclass experiments on two publicly available datasets, the feature refinement strategy is discussed. Compared to existing deep learning-based intrusion detection methods, our approach demonstrates advantages in both intrusion detection performance and computational complexity, highlighting its deployment potential in embedded IVN environments.

In the field of industrial control, edge computing puts forward higher requirements for the local processing ability of data. The traditional industrial programmable logic controller (PLC) cannot complete this task. The edge intelligent controller (EIC) is developed according to the demand for edge computing. As the core component of edge computing, the security and reliable operation of EIC has great significance and influence on the promotion and development of edge computing. For the access authentication of EIC, an efficient revocable anonymous authentication scheme for EICs by utilizing a group signature technique is proposed in this article, which can trace the identity information of the EIC and protect the privacy of the EIC, and apply an efficient revocation mechanism to revoke any illegal or abnormal EICs. Through security analysis, we prove that the authentication scheme is anonymity, integrity, traceability, forward secrecy, resistance to the replay attack, and efficient revocability. Furthermore, the performance analysis and comparisons show that the authentication scheme is efficient and feasible for EICs.

The Internet of Things (IoT) network intrusion detection (NID) has emerged as a powerful technique for effectively detecting attacks and protecting internet safety across a wide range of applications. However, existing NID models often struggle to achieve good detection performance when deployed in resource-restricted environments. To achieve an optimal balance between detection performance and computational cost, we propose a lightweight knowledge distillation bidirectional encoder representations from Transformers (KD-BERT) framework for efficient IoT intrusion detection. Specifically, we first develop a header-payload pairs tokenization method to preprocess the raw data. Then, we distill rich knowledge from a large pre-trained $\text{BERT}_{\text{teacher}}$ model into a smaller $\text{BERT}_{\text{student}}$ model with fewer training parameters. By leveraging KD-BERT, the $\text{BERT}_{\text{teacher}}$ can be compressed into the $\text{BERT}_{\text{student}}$, reducing the number of parameters from 135 to 9 M, while maintaining high detection accuracy. To effectively obtain the essential features, we design a data transformation strategy and fine-tuned the $\text{BERT}_{\text{student}}$ model on several labeled IoT NID tasks. Comprehensive experimental results on three public datasets (telemetry datasets, operating systems datasets and network traffic dataset of IoT (TON-IoT), Edge-IIoTset, and IoMT2024) have demonstrated superior performance compared to state-of-the-art methods.