In recent years, Cyberspace Endogenous Security (CES) has emerged as a disruptive technology reshaping the security defense paradigm, challenging and reconstructing the existing cybersecurity theoretical system at an unprecedented pace. Traditional security technologies have long relied on “patching” and “plugging vulnerabilities”—an external add-ondefense logic. In contrast, endogenous security theory introduces the Dynamic Heterogeneous Redundancy (DHR) architecture, transforming security attributes into inherent immune capabilities of the system structure. This paper presents a complete English translation with mathematical proofs from control theory ultrastability, system theory M¨obius ring effect, information theory entropy confrontation, uncertainty principle, perfect security equivalence,and game-theoretic Nash equilibrium, while discussing open problems and future outlook for generalized functional safety.
School abductions in Nigeria persistently undermine educational continuity and human security. While existing scholarship has examined the drivers and consequences of kidnapping, structured forecasting tools for proactive school protection are lacking. This study addresses that gap by developing a time series early warning framework using documented incident data from January 2013 to December 2021. Incident records from verified public domain sources were aggregated into a monthly time series and analyzed using an ARIMA (1,1,1) model in Python. Model identification was guided by autocorrelation diagnostics and by minimizing the Akaike Information Criterion. Forecast performance was evaluated using a temporally ordered 70/30 training–testing split with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as validation metrics. The model achieved a training RMSE of 34.99 and a testing RMSE of 96.81. The higher testing RMSE, while reflecting sensitivity to unforeseen escalation spikes, is acceptable for a probabilistic early warning tool where sensitivity is prioritized over exact precision. Findings reveal that school abductions exhibit structured temporal dependence and cyclical escalation–de escalation dynamics rather than random occurrence. Five-month-ahead forecasts generate probabilistic risk windows aligned with historical volatility patterns. Although not intended to predict specific attack events, the framework demonstrates that historical incident data can be transformed into actionable early warning signals for anticipatory school security governance. The study contributes to education sector security research by advancing a shift from reactive post-incident response toward predictive, data-informed protection strategies in conflict-sensitive environments.
This paper addresses the distributed control problem of multi-area power systems integrated with virtual power plants (VPPs) under the coexistence scenario of denial-of-service (DoS) attack and actuator failure. To ensure the operational performance of multi-area power systems subject to such attack and actuator failure, a distributed control strategy integrated with load frequency control (LFC) is proposed. Based on Lyapunov stability theory, sufficient conditions for the bounded stability of multi-area power systems are derived. Furthermore, the controller gain parameters are solved by using the linear matrix inequality (LMI) approach. Finally, the effectiveness of the proposed theoretical method is verified through a case study on a two-area power system.
Amidst the intelligent revolution reshaping the transportation industry, safety remains the top priority in vehicle manufacturing. International standards ISO 26262 (Road vehicles – Functional safety), ISO 21448 (Road vehicles – Safety of the intended functionality), and national standard GB/T 34590 (Road vehicles – Functional safety) define functional safety design and development processes for individual electronic/electrical systems. However, from an industry-wide perspective, the practice of treating passenger vehicles as complex integrated systems for functional safety analysis remains relatively limited. In particular, the collaborative application of ISO 26262 and SOTIF is still in the exploratory and refining stage. To a certain extent, this situation not only escalates the difficulty of identifying cross-system risks but also poses challenges to the development of a vehicle-level functional safety protection framework. This study focuses on the application of System-Theoretic Process Analysis (STPA) in vehicle-level functional safety analysis. By mapping its application pathways, we define a safety analysis methodology that integrates STPA with Hazard Analysis and Risk Assessment (HARA), enabling a shift from traditional single-system safety analysis to a system-engineering-aligned vehicle-level safety analysis. The proposed methodology effectively identifies vehicle-level functional safety risks, addressing a critical gap in industry practices. This research provides theoretical support and practical pathways for safety design in the era of intelligent vehicles.
The development of quantum computing poses a serious threat to traditional cryptographic systems and has driven the rapid development of Post-Quantum Cryptography (PQC). Specialized hardware solutions, especially PQC chips, are considered a key to achieving secure and efficient migration of quantum cryptography. This review first introduces the relevant background and the main PQC schemes and then focuses on analyzing the design challenges and bottlenecks faced by the design of PQC chips. Next, the development status and unresolved issues of different technologies, such as Central Processing Unit (CPU) Instruction-Set Extension (ISE), Field-Programmable Gate Array (FPGA), Application-Specific Integrated Circuits (ASIC), and Domain-Specific Accelerators (DSA), are further organized and analyzed. Finally, on the basis of specific application requirements, the future development technology path of PQC chips is discussed.
Today, everyone is heavily dependent on computers, mobile devices, and digital systems/applications to store, access, and transmit their data and personal information. On the other hand, cybersecurity threats, exploitation of digital systems, and new, complex cyberattacks are evolving daily. This requires a growing need for innovative approaches to protecting data beyond traditional methods. The study explores the use of biometric systems in cybersecurity for the prevention and detection of cyberattacks by integrating them for authenticating and authorizing individuals. Biometric authentication is used to verify an individual's identity and grant them access based on their roles or authorizations. The paper focuses on understanding current trends and emerging technologies in biometric systems, while recognizing that these systems are not immune to cyberattacks. Can synthetic biometric data that is generated using virtual identities be an option to be considered to minimize the risk of exposing user identity in the event of a data breach, and as a means to preserve privacy, be explored as part of this research? A qualitative study is carried out using existing literature and analyzed based on the generated themes. The outcome of the study resulted in a multi-layered conceptual framework integrating the modalities of biometric systems with synthetic data and a model offering a feedback loop that can enhance operational efficiency and cultivate user trust and resilience. The study also provides insights relevant to businesses and researchers to build their systems and enhance research from a user perspective.
C-V2X (Cellular Vehicle-to-Everything) has become a profound reform and innovative technology in automotive and communication industries. Through C-V2X communication, the nearby traffic participants can exchange real-time information, such as status, positioning, location, to achieve environmental perception, information exchange, and collaborative control capabilities. However, because of the rapid changes in traffic topology and high vehicle mobility, the improvement of safety and efficiency in the distributed C-V2X system is complex and dynamic. The information shared among the participants requires a systematic technical solution to solve the untrustworthy issues. In order to address the challenges, this paper focuses on the trust mechanism in the C-V2X system. It analyzes the latest progress of research on trust mechanism of C-V2X in academia and industries. A trustworthiness evaluation framework oriented to lifecycle management of trust models is proposed, including trust information management and analysis, trust models management, trustworthiness evaluation, and trust decision. Taking two typical C-V2X scenarios as examples, this paper illustrates the detailed trust assessment process with different modules in the system. Finally, the open and important research issues are proposed for the future trust mechanism of C-V2X.
Many modern complex systems, such as advanced driver assistance systems (ADAS), face the problems of dynamic and diverse test environments and unpredictable test results when performing black-box testing. This may lead to imprecise fault localization and low test efficiency. Inspired by the Dynamic Heterogeneous Redundancy (DHR) architecture in the field of network security, this paper proposes a contrastive testing method to solve these problems. The core innovation of this method is the concept transfer from ``fault tolerance redundancy" to ``error detection redundancy." This approach uses a set of functionally equivalent but heterogeneous executors to expose defects in the Unit under Test (UUT) through output inconsistencies. The proposed framework is built on a hierarchical system architecture, which supports progressive testing from the top module to the bottom module to complete the precise localization of defects. The heterogeneity of these reference implementations (RIs) is the key to reducing common mode failures, which is ensured by a multidimensional quantization model. The majority consensus adjudication automates defect detection by treating the output of the majority of RIs as a behavioral baseline, eliminating the need for a priori expectations. Experimental results on an ADAS show that our approach reduces the testing time by 63.9% and successfully transitions the DHR architecture from cybersecurity to the testing domain, providing a robust and scalable solution for testing heterogeneous and uncertain systems under intellectual property (IP) constraints.
Security is of paramount importance to the Internet of Vehicles (IoV), leading to the proposal of the Dual-S architecture integrating functional safety and cybersecurity. However, complex resource competition, collaborative interactions, irreconcilable external adversarial interactions, and mutual outcome transmission across these two security levels lack a unified descriptive and analytical framework. To address this gap, we propose DNAS2–a Dual-Nature Alliance Security and Strategy Framework for IoV security–based on mainstream game theory, which consists of two interconnected core modules: in the Internal Cooperative Game Module, we formalize the Dual-S Alliance with collective rationality prioritized over individual interests, propose an equilibrium solution method for coalition benefit allocation, and prove that this method can drive the endogenous security system toward Pareto optimality; in the External Non-Cooperative Attack-Defense Game Module, we construct a model incorporating dynamic strategy evolution and information asymmetry in attack-defense confrontations, derive equilibrium solutions, and provide a mathematical basis for optimal defense strategy formulation and payoff prediction. The unified analytical tool we propose supports the optimization of IoV security schemes, and future work will focus on model refinement, exploring its applications in evolutionary game scenarios, and promoting its engineering implementation.
As autonomous driving systems evolve towards higher levels of autonomy, large language models (LLMs) are increasingly being introduced for understanding complex traffic scenarios and motion prediction, demonstrating their potential in processing multimodal and unstructured information. The core argument of this paper is that the inherent characteristics of LLMs, such as their susceptibility to hallucinations and extreme sensitivity to input perturbations, fundamentally conflict with the determinism and robustness sought by classical control theory. This conflict is further amplified in the context of cyberattacks, potentially jeopardizing the closed-loop stability and safety of autonomous driving systems. This paper will discuss these issues from the core dimensions of control theory, including attack-detection-defend and control system, aiming to provide a critical perspective and potential research directions for building safe, reliable, and trustworthy next-generation intelligent transportation systems (ITS).
The rapid advancement of digital technologies and the growing complexity of financial systems have amplified the uncertainty, heterogeneity, and interconnectedness of modern financial problems. Traditional digital finance methods, which typically focus on single scenarios, isolated user groups, or narrowly defined tasks, struggle to address today’s hybrid financial environments characterized by cross-scenario demands, multi-stakeholder interactions, and tightly coupled functional and security requirements. To bridge this gap, we first propose a novel taxonomy of digital finance research encompassing five macro perspectives (Application, Model, Element, Platform Infrastructure, and Facility), providing a systematic coordinate system for positioning and analyzing existing studies. Building on this foundation, we introduce ℱAMEπ, a practical full-stack system architecture designed to empower secure, scalable, and holistic digital finance. ℱAMEπ co-designs and integrates diverse financial applications, a broad spectrum of models, theories of full-element aggregation optimization and trustworthy computation, and a unified intelligent computing platform. Unlike fragmented or patchwork solutions, ℱAMEπ achieves cross-layer interoperability and system-level optimization, enabling resilient, robust, and inherently secure digital finance across all major scenarios. This work thus establishes both a conceptual blueprint and an architectural path toward next-generation full-stack digital finance systems.
The proliferation of electric vehicles in recent years has significantly expanded the charging infrastructure while introducing new security risks to both vehicles and chargers. In this paper, we investigate the security of major charging protocols, including SAE J1772, CCS, IEC 61851, GB/T 20234, and NACS, and uncover new physical signal spoofing attacks in their authentication mechanisms. By inserting a compact malicious device into the charger connector, attackers can inject fraudulent signals to disrupt the charging process, resulting in denial-of-service, vehicle-induced charger lockout, and damage to the chargers or the vehicle’s charge management system. To demonstrate the feasibility of our attacks, we propose PORTulator, a proof-of-concept (PoC) attack hardware, including a charger gun plugin device for injecting physical signals and a wireless controller for remote manipulation. By evaluating PORTulator on multiple real-world chargers, we identify 7 charging standards used by 20 charger piles that are vulnerable to our attacks. These attack primitives are not merely protocol flaws, but practical cyber-physical threats that can be leveraged for service disruption, extortion, and targeted sabotage in public or fleet-dependent charging environments. The root cause is that chargers use simple physical signals for authentication and control, making them easily spoofed by attackers. To address this issue, we propose enhancing authentication circuits by integrating non-resistive memory components and utilizing dynamic high-frequency Pulse Width Modulation (PWM) signals to counter such physical signal spoofing attacks.
This study aimed to analyze the nature, scale, and consequences of cyberattacks on critical cyber-physical systems in Ukraine over the past decade, using a methodology based on classifying attacks by type, threat actor (including Russian hacking groups Sandworm, Fancy Bear, and Ember Bear responsible for half of the 22 analyzed incidents), target sector, and temporal patterns. It also included comparative analysis of cyber defense strategies. The Chinese group Volt Typhoon also demonstrated high risk through living-off-the-land techniques. While phishing remained the primary attack vector (7 cases), sophisticated supply chain attacks like NotPetya caused significant damage, with the energy sector being most targeted (7 incidents) due to its strategic importance. Six attacks involved manipulation of Industrial Control Systems/Operational Technology protocols, while four employed destructive wiper malwares. The Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege (STRIDE) analysis of digital platforms concluded that modern challenges require innovative solutions like Cybersecurity Mesh Architecture, digital immunity systems, and artificial intelligence, along with international coordination, while addressing barriers such as legacy systems, workforce shortages, and regulatory fragmentation, ultimately providing an evidence base for improving cybersecurity strategies at national and international levels.
This review systematically examines the multifaceted cyber-physical security risks facing new power systems during the global energy transition, with a focus on three critical dimensions. First, it elucidates real-world instances of Cyber-Physical Coupling Threat (CPCT) to highlight the emergent security challenges associated with transformations and distribution paradigms in power generation. Second, it dissects the traditional security risks at each phase within the Sensing-Transmitting-DecisionMaking-Controlling (STDC) loop, offering a thorough examination of vulnerabilities and potential attack vectors. Third, it explores emerging threats from the Generation-Grid-Load-Storage (GGLS) integration model, which pose severe threats to the stable operation of power system. Finally, synthesizing these insights, this study outlines six future risk categories that demand proactive defense strategies. These findings advance theoretical frameworks to bolster the resilience of low-carbon energy systems, emphasizing adaptive cybersecurity measures for stable and secure power grid operations.
True Random Number Generators (TRNGs) are essential components in industrial systems and security-critical applications, providing non-deterministic randomness based on physical phenomena such as electronic noise, quantum effects, and biological processes. Unlike Pseudo-Random Number Generators (PRNGs), TRNGs offer stronger unpredictability, making them crucial in areas such as industrial control systems (ICS), secure communications, and block-chain protocols. This survey provides a comprehensive review of TRNG technologies. It covers various types of TRNGs and their physical principles, traces their historical development from early hardware to modern implementations, and examines widely used statistical and visual analysis methods for evaluating randomness. We also discuss key challenges in TRNG development, including entropy source reliability, hardware limitations, and scalability for real-world deployment. Furthermore, we explore TRNG applications in block-chain systems, where they support tamper-resistant operations such as consensus, smart contracts, and device authentication. We also highlight the growing integration of TRNGs with machine learning techniques, both to improve randomness generation and to monitor and analyze TRNG output. Overall, this review aims to provide researchers and practitioners with a clear and structured understanding of TRNGs, emphasizing their importance in modern digital and industrial environments.
Industrial control systems (ICSs) are designed for monitoring and controlling industrial processes, enabling the automation and management of critical sectors such as production, manufacturing, and power system through electronic devices and communication infrastructure. Industrial control protocols (ICPs) refer to the standardized rules and formats used for communication. Protocol reverse engineering (PRE) refers to the process of inferring the structure, semantics, and behavior of a communication protocol in the absence of official specifications or documentation. Given the prevalence of proprietary protocols in ICS and the limited formal documentation, PRE is an important method for understanding and managing protocol behavior in complex heterogeneous industrial environments. Over the past decades, ICSs have typically operated within isolated and closed network environments, where many protocol specifications remained proprietary and unknown, thereby hindering the evaluation of protocol security. This limitation has driven the development of reverse engineering approaches for ICPs. To systematically summarize the current research results and development of ICP reverse engineering, we build a complete technical framework about typical objectives of protocol reverse engineering. The existing methods are summarized in seven aspects, including data acquisition, message clustering, field division, key field identification, field semantic derivation, state machine modeling, and application. The common problems and limitations are discussed, and finally, combined with future implementation needs, we propose several research directions worthy of attention.
