A device’s intrinsic security fingerprint, representing its physical characteristics, serves as a unique identifier for user devices and is highly regarded in the realms of device security and identity recognition. However, fluctuations in the environmental noise can introduce variations in the physical features of the device. To address this issue, this paper proposes an innovative method to enable the device’s intrinsic security fingerprint to adapt to environmental changes, aiming to improve the accuracy of the device’s intrinsic security fingerprint recognition in real-world physical environments. This paper initiates continuous data collection of device features in authentic noisy environments, recording the temporal changes in the device’s physical characteristics. The problem of unstable physical features is framed as a restricted statistical learning problem with a localized information structure. This paper employs an aggregated hypergraph neural network architecture to process the temporally changing physical features. This allows the system to acquire aggregated local state information from the interactive influences of adjacent sequential signals, forming an adaptive environment-enhanced device intrinsic security fingerprint recognition model. The proposed method enhances the accuracy and reliability of device intrinsic security fingerprint recognition in outdoor environments, thereby strengthening the overall security of terminal devices. Experimental results indicate that the method achieves a recognition accuracy of 98% in continuously changing environmental conditions, representing a crucial step in reinforcing the security of Internet of Things (IoT) devices when confronted with real-world challenges.

This paper explores an event-triggered load frequency control (LFC) strategy for smart grids incorporating electric vehicles (EVs) under the influence of random deception attacks. The aggressive attack signals are launched over the channels between the sensor and controller, compromising the integrity of transmitted data and disrupting LFC commands. For the purpose of addressing bandwidth constraints, an event-triggered transmission scheme (ETTS) is developed to minimize communication frequency. Additionally, to mitigate the impact of random deception attacks in public environment, an integrated networked power grid model is proposed, where the joint impact of ETTS and deceptive interference is captured within a unified analytical structure. Based on this framework, a sufficient condition for stabilization is established, enabling the concurrent design of the ${\mathcal{H}}_{\infty }$ controller gain and the triggering condition. Finally, two case studies are offered to illustrate the effectiveness of the employed scheme.

Recent advances in technologies such as blockchain, the Internet of Things (IoT), Cyber–Physical Systems (CPSs), and the Industrial Internet of Things (IIoT) have driven the digitalization and intelligent transformation of modern industries. However, embedded control devices within power system communication infrastructures have become increasingly susceptible to cyber threats due to escalating software complexity and extensive network exposure. We have seen that symmetric conventional patching techniques—both static and dynamic—often fail to satisfy the stringent requirements of real-time responsiveness and computational efficiency in resource-constrained environments of all kinds of power grids. To address this limitation, we have proposed a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method has integrated binary-level vulnerability modeling, execution-trace-driven fault localization, and lightweight patch synthesis, enabling dynamic, in-place code redirection without disrupting ongoing operations. By constructing a system-level instruction flow model, the framework has leveraged on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture have revealed that the proposed approach has reduced patch latency by 92% over static techniques, which are symmetrical in a working way, while incurring less than 3% CPU overhead. This work has offered a scalable and real-time model-driven defense strategy that has enhanced the cyber–physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability.

Internet of things (IoT) devices are widely used in various fields, with their growing diversity and complexity posing challenges for traditional security measures. Device fingerprint identification can enhance network security and reliability by verifying device features. However, traditional device fingerprint identification methods usually rely on a single mode of traffic characteristics. In the face of changing network environments and diversified device types, it is often difficult to ensure efficient identification performance and robustness. To address the above challenges, this paper proposes a multi-modal traffic classification method for device identification in IoT networks to address the challenges in accuracy and robustness posed by traditional single-modal traffic feature approaches. The method combines various traffic features, such as packet size, transmission interval, flow duration, packet rate, byte rate, and protocol number. It includes four modules: data collection, preprocessing, model training, and fingerprint identification. Network traffic data are collected using deep packet inspection and capture tools, and features are standardized. The bidirectional encoder representations from transformers (BERT) model is applied for sensitive text feature extraction, while the convolutional neural network (CNN) model aids in device identification. Experimental results demonstrate high accuracy and robustness across different network environments and device types.

The integration of IoT sensing in energy systems has created critical security challenges for sensitive operational data, particularly in industrial energy networks where anonymous access control must balance privacy protection with system reliability. Traditional approaches suffer from high performance overhead and vulnerability to cyberattacks, posing significant risks to energy infrastructure. This study proposes a dynamic anonymous access control framework combining dual-mode single-packet authentication with adaptive trust evaluation. Our method achieves 37% lower communication overhead and 41% reduced computational costs compared to UDP-based SDP schemes, while maintaining robust security through continuous trust assessment. Experimental results demonstrate the framework’s effectiveness in protecting energy system data flows against identity tracing and unauthorized access, making it particularly suitable for smart grid and industrial energy applications where both efficiency and security are paramount. The proposed solution advances the state-of-the-art in zero-trust architectures for critical energy infrastructure.

The integrated terminal provides the bi-directional communication between master station in power grid and the edge-side equipment in micro-grid. Thus, it is vital to guarantee the safety of the integrated terminal. This paper firstly investigates the micro-grid scenario to analysis the potential vulnerabilities of the integrated terminal. Then, a penetration test is implemented to clarify these vulnerabilities and evaluating the severity of these vulnerabilities. Finally, this paper offers some state-of-art protection techniques to enhance the safety of the micro-grid.