Yunfan Wang

Industrial control protocol messages often contain shared headers while exhibiting function-dependent layouts in downstream fields. Such variations make byte-level similarity insufficient for recovering field structures and semantic roles when protocol specifications are unavailable. This paper formulates unknown industrial protocol analysis as a relation-constrained semantic parsing problem and develops an evidence-driven self-supervised semantic parser for passive traffic analysis. The proposed framework first discovers latent functional message families from observable traffic regularities and uses them as intermediate representations for subsequent parsing. A relation-aware segment graph is then constructed to model candidate field units and integrate structural dependencies within message families, across functional families, and between request-response interactions. The graph-based inference process refines field boundaries by combining local byte evidence with relational constraints. A consistency-aware semantic mapping mechanism further assigns protocol roles to recovered fields based on role-specific evidence and structural dependencies while preserving uncertain fields as unknown. Experiments on Modbus/TCP, IEC 104, MQTT, and S7comm demonstrate that the proposed method achieves a mean V-measure of 0.95 for message-family discovery, a boundary F1-score of 0.91 for field reconstruction, and a semantic macro-F1-score of 0.88 for field role identification.

The evolution of industrial automation toward Industry 3.0 and 4.0 has driven the emergence of Industrial Edge-Cloud Platforms, which increasingly depend on web interfaces for managing and monitoring critical operational technology. This convergence introduces significant security risks, particularly from Broken Access Control (BAC)—a vulnerability consistently ranked as the top web application risk by the Open Web Application Security Project (OWASP). BAC flaws in industrial contexts can lead not only to data breaches but also to disruptions of physical processes. To address this urgent need for robust web-layer defense, this paper presents VeriFlow, a static verification framework for access control in web applications. VeriFlow reformulates access control verification as a consistency problem between two core artifacts: (1) a Formal Access Control Policy (P), which declaratively defines intended permissions, and (2) a Navigational Graph, which models all user-driven UI state transitions. By annotating the graph with policy P, VeriFlow verifies a novel Path-Permission Safety property, ensuring that no sequence of legitimate UI interactions can lead a user from an authorized state to an unauthorized one. A key technical contribution is a static analysis method capable of extracting navigational graphs directly from the JavaScript bundles of Single-Page Applications (SPAs), circumventing the limitations of traditional dynamic crawlers. In empirical evaluations, VeriFlow outperformed baseline tools in vulnerability detection, demonstrating its potential to deliver strong security guarantees that are provable within its abstracted navigational model. By formally checking policy-graph consistency, it systematically addresses a class of vulnerabilities often missed by dynamic tools, though its effectiveness is subject to the model-reality gap inherent in static analysis.

Within the framework of a dynamic memory-event-triggered mechanism (DMETM), this paper proposes an estimate-based secure control algorithm for nonlinear networked control systems (NNCSs) that suffer from hybrid attacks. Firstly, a sampled-data observer is employed utilizing the output signals to estimate the states. Secondly, due to the limitation of data transmission capacity in NNCSs, a novel DMETM with auxiliary variable is proposed, which effectively leverages the benefits of historical sampled data. In the process of network data transmission, a hybrid attack model that simultaneously considers the impact of both deception and denial of service (DoS) attacks is introduced, which can undermine signal integrity and disrupt data transmission. Then, a memory-event-triggered controller is developed, and the mean square stability of the NNCSs can be ensured by selecting some appropriate values. Finally, a numerical simulation and a practical example are given to illustrate the meaning of the designed dynamic memory-event-triggered control (DMETC) algorithm.

The collaborative construction of large-scale, diverse datasets is crucial for developing high-performance machine learning models. However, this collaboration faces significant challenges, including ensuring data security, protecting participant privacy, maintaining high dataset quality, and aligning economic incentives among multiple stakeholders. Effective risk management strategies are essential to systematically identify, assess, and mitigate potential risks associated with data collaboration. This study proposes a federated blockchain-based framework designed to manage multiparty dataset collaborations securely and transparently, explicitly incorporating comprehensive risk management practices. The proposed framework involves six core entities—key distribution center (KDC), researcher (RA), data owner (DO), consortium blockchain, dataset evaluation platform, and the orchestrating model itself—to ensure secure, privacy-preserving and high-quality dataset collaboration. In addition, the framework uses blockchain technology to guarantee the traceability and immutability of data transactions, integrating token-based incentives to encourage data contributors to provide high-quality datasets. To systematically mitigate dataset quality risks, we introduced an innovative categorical dataset quality assessment method leveraging label reordering to robustly evaluate datasets. We validated this quality assessment approach using both publicly available (UCI) and privately constructed datasets. Furthermore, our research implemented the proposed blockchain-based management system within a consortium blockchain infrastructure, benchmarking its performance against existing methods to demonstrate enhanced security, reliability, risk mitigation effectiveness, and incentive alignment in dataset collaboration.

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization.

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.

Embedded control devices within power system communication infrastructures are increasingly vulnerable to cyber threats due to escalating software complexity and pervasive network exposure. Conventional patching mechanisms—whether static or dynamic—often fail to reconcile the competing demands of real-time responsiveness and computational efficiency in resource-constrained environments typical of power grids. To tackle this issue, we propose a hardware-assisted runtime patching framework tailored for embedded systems in critical power system networks. Our method integrates 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 leverages on-chip debug registers to deploy patches at runtime, ensuring minimal operational impact. Experimental evaluations within a simulated substation communication architecture reveal that the proposed approach reduces patch latency by 92% over static techniques, while incurring less than 3% CPU overhead. This study offers a scalable and real-time model-driven defense strategy that enhances the cyber-physical resilience of embedded systems in modern power systems, contributing new insights into the intersection of runtime security and grid infrastructure reliability.

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.