Search Results (47)

This paper investigates an optimal cooperative guidance strategy for the active defense of an early-warning aircraft (EWA) escorted by two fighters against an incoming missile. The proposed framework extends classical three-body defense models (Target–Missile–Interceptor) into a more realistic four-body engagement (Target–Missile–Interceptor 1–Interceptor 2), allowing explicit coordination among multiple defenders. By projecting the 3D engagement kinematics onto two orthogonal 2D planes—a validated simplification for typical aerial combat geometries—a tractable dynamic model is obtained. Within this model, an analytical cooperative guidance law is derived using optimal control theory and the calculus of variations, minimizing a multi-objective cost function that combines miss distance, control effort, intercept geometry, and coordination terms. Extensive Monte Carlo simulations across 23 attack directions and multiple initial ranges demonstrate that the proposed method achieves an interception success rate of 99%, with an average miss distance of below 5 m. Robustness tests further confirm stable performance under target maneuver uncertainty, sensor noise, and modeling deviations. The algorithm features closed-form control commands with low computational complexity, enabling real-time onboard implementation. Full article

Aramid fiber reinforced polymers (AFRPs) are widely used in aerospace and defense structures because of their high specific strength, impact resistance, and damage tolerance. However, severe burr formation during machining remains a major obstacle to achieving high surface integrity and dimensional accuracy. In particular, the mechanism by which tool edge radius affects burr formation in AFRP cutting has not yet been clarified quantitatively. To address this issue, this study develops an analytical model for the orthogonal cutting of AFRPs to reveal the burr formation mechanism associated with tool edge radius. The model, established on the basis of contact mechanics and fracture theory, predicts fiber deflection, cutting force evolution, fracture behavior, and burr length under different contact and boundary conditions. The results show that tool edge radius governs burr formation through a contact–state transition mechanism. When the edge radius is below a critical threshold, localized point-contact-like interaction promotes stress concentration and fiber fracture, leading to relatively clean material removal. When the edge radius exceeds this threshold, the interaction evolves toward extended contact and sliding, which suppresses complete fiber fracture and results in pronounced burr retention. Experimentally, increasing the edge radius from 5.6 μm to 110.3 μm increased the maximum burr height from 3.19 μm to 83.58 μm, corresponding to an increase of approximately 2520%. The predicted burr evolution agrees well with the experimental observations in both trend and characteristic magnitude. This study provides a mechanistic and predictive understanding of burr formation in AFRP machining and offers practical guidance for cutting edge preparation, tool wear control, and process optimization in high-quality composite machining. Full article

As the core infrastructure for providing all-weather, full-coverage, high-speed, and diversified information services, spatial information networks (SINs) possess significant social, economic, and military value. However, due to the inherent characteristics of their network architecture, SINs are susceptible to core service paralysis and functional failure under large-scale targeted attacks or random disturbances, posing a critical bottleneck that constrains their stable operation. Current research on SIN vulnerability is predominantly confined to a single network topology perspective, lacking an integrated consideration of the task execution perspective. Consequently, it fails to accommodate the dual requirements of “network topology stability” and “task execution effectiveness”. To address the aforementioned research needs and challenges, this study adopts a “topology-task” dual-perspective fusion approach and proposes a vulnerability analysis framework for SINs that integrates multi-layer networks and hypernetworks. First, a two-layer SIN topology model encompassing the user layer and the satellite layer is constructed. Leveraging hypernetwork theory, information tasks involving multiple network entities are formally defined, and an integrated multi-layer hypernetwork model is established. Second, based on distinct task types, three categories of task efficiency evaluation metrics are defined, and corresponding quantitative methods for calculating SIN vulnerability are derived. Third, during the vulnerability analysis phase, a novel strategy for identifying and removing overlapping nodes in hypernetworks is introduced to enable precise localization of critical nodes within the network. Concurrently, a pre-attack node hardening strategy is designed to minimize the impact of attacks on network performance. Finally, through systematic analysis of vulnerability performance and critical node characteristics under different node removal strategies, the results demonstrate enhanced network performance. The effectiveness of the proposed method is validated by comparing the defense performance of the hardening strategy across various attack scenarios. To verify the feasibility and superiority of the proposed method, this study designs 5 × 5 groups of simulation experiments with varying network parameters. The results indicate that, compared with traditional methods, the proposed strategy can more accurately identify core nodes affecting the stable operation of SINs, significantly reducing network vulnerability and improving network survivability. In addition, a comprehensive sensitivity analysis of SIN vulnerability is conducted from three key influencing dimensions—mission scale, satellite count, and constellation configuration—clarifying the impact of each dimension on network invulnerability. Thus, this paper provides a reliable theoretical foundation and technical support for the planning, design, optimal deployment, and operation and maintenance management of SINs. Full article

Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized for procedural accuracy and surface-level task performance rather than the macrocognitive processes that underpin adaptive expertise. Macrocognition encompasses higher-order cognitive processes that are essential for performance transfer beyond controlled training conditions. When these processes are insufficiently supported, training systems risk fostering brittle strategies and negative training effects. This paper introduces a macrocognitive design taxonomy for simulation-based training systems derived from a large-scale meta-analysis examining the transfer of macrocognitive skills from immersive simulations to real-world training environments. Drawing on evidence synthesized from 111 studies spanning healthcare, industrial safety, skilled trades, and defense contexts, the taxonomy links macrocognitive theory to human–computer interaction (HCI) design affordances, computational data traces, and feedback and adaptation mechanisms shown to support transfer. Grounded in joint cognitive systems theory and learning engineering practice, the taxonomy treats macrocognition as a designable and computable system concern informed by empirical transfer effects rather than as an abstract explanatory construct. Full article

Multiple factors, including global climate change and rising sea levels, increase the frequency and intensity of seawater intrusion. However, most previous studies have not regarded seawater intrusion as a typical accident for systematic safety risk assessment. In view of this, this paper comprehensively employed the fault tree model, the “Man-Machine-Material-Method-Environment” system theory, the Bayesian network model, and the Attack-Defense game theory to conduct qualitative and quantitative analyses concerning seawater intrusion. The main research results were as follows: the fault tree model was applied to sort the hazard-inducing factors from the perspectives of nature and humans, which were further categorized based on the theoretical framework of the “Man-Machine-Material-Method-Environment” (4M1E) theory. Based on the Bayesian network model, and incorporating assessments of the existing defense conditions of the target port, the occurrence probability of seawater intrusion at the port was calculated as 3.05%. Simultaneously, the influence weights of the 4M1E factors on seawater intrusion were quantified; environmental and mechanical factors ranked as the top two contributors, accounting for 57.79% and 29.70% of the total impact, respectively. Utilizing the Attack-Defense game theory, two key risk evolution paths of seawater intrusion were identified, with occurrence probabilities of 7.627‰ and 4.164‰. Typical disaster cases clarified the underlying mechanisms by which these key risk paths triggered seawater intrusion, and targeted prevention and control measures were proposed accordingly. The research findings can not only deepen the systematic understanding of seawater intrusion as a typical marine disaster but also provide technical references for governments and enterprises to optimize their risk management systems. Full article

As the digitalization and intelligent transformation of power systems accelerates, the Power Internet of Things (PIoT) plays a pivotal role in ensuring efficient energy transmission and real-time regulation. However, this openness and interconnectivity also expose the system to diverse cyber threats, where attackers can disrupt stable power communication and dispatch operations through means such as data tampering, denial-of-service attacks, and control intrusion. To characterize the dynamic adversarial process between attackers and defenders in the PIoT, this paper constructs a zero-sum differential game model for cyber attack–defense strategy selection. To achieve equilibrium in the formulated differential game, optimal control theory is employed to solve the optimization problems of the game participants, thereby deriving the optimal strategies for both attackers and defenders. Finally, simulation results illustrate the evolution of network resource competition between attackers and defenders in the PIoT. The results also demonstrate that our proposed model can effectively and accurately describe the evolution of the system security state and the impact of strategic interactions between attackers and defenders. Full article

Software-defined (SD) honeypots, as dynamic cybersecurity technologies, enhance defense efficiency through flexible resource allocation. However, traditional SD honeypots face latency and jitter issues under network fluctuations, while balancing adjustment costs with defense benefits remains challenging. This paper proposes a DPU-accelerated SD honeypot security service deployment method, leveraging DPU hardware acceleration to optimize network traffic processing and protocol parsing, thereby significantly improving honeypot environment construction efficiency and response real-time performance. For dynamic attack–defense scenarios, we design an adaptive adjustment strategy combining Stackelberg game theory with deep reinforcement learning (AASGRL). By calculating the expected defense benefits and adjustment costs of optimal honeypot deployment strategies, the approach dynamically determines the timing and scope of honeypot adjustments. Simulation experiments demonstrate that the mechanism requires no adjustments in 80% of interaction rounds, while achieving enhanced defense benefits in 20% of rounds with controlled adjustment costs. Compared to traditional methods, the AASGRL mechanism maintains stable defense benefits in long-term interactions, verifying its effectiveness in balancing low costs and high benefits against dynamic attacks. This work provides critical technical support for building adaptive proactive network defense systems. Full article

This multidisciplinary study investigates Piper rivinoides, a Brazilian medicinal species, focusing on its chemical composition and antimycobacterial potential. UHPLC-HRMS/MS of leaves, stems, branches, and roots revealed 58 compounds, including neolignans, lignanamides, triterpenes, flavonoids, and carotenoids. Fourteen metabolites, notably benzofuran neolignans and pentacyclic triterpenes are annotated here for the first time. Quantitative analyses by HPLC-DAD-UV showed that eupomatenoid-5, eupomatenoid-6, and conocarpan were most abundant in leaves, reaching amounts approximately twice those found in branches and stems and about ten times higher than in roots, supporting the optimal defense theory and organ-specific accumulation of bioactive metabolites. Biological assays against Mycobacterium tuberculosis strains H37Rv and M299 revealed strong inhibitory activity for the leaf extract and isolated neolignans. Eupomatenoid-5 and eupomatenoid-6 achieved inhibition comparable to rifampicin, with low MIC₅₀ values, while conocarpan exhibited moderate activity. Antimycobacterial effects were more pronounced against the H37Rv strain, although relevant inhibition was also observed for the hypervirulent M299 strain. These findings highlight P. rivinoides as a rich source of benzofuran neolignans and promising antimycobacterial properties. The integration of advanced mass spectrometric analyses with bioassays demonstrates the value of combining chemical and biological approaches to uncover novel natural products. The putative identification of new neolignans and triterpenes, along with the confirmation of potent antimycobacterial activity, provides a robust foundation for further studies on biosynthesis, structure–activity relationships, and potential biotechnological applications. Full article

Moving Target Defense (MTD) has been proposed as a dynamic defense strategy to address the static and isomorphic vulnerabilities of networks. Recent research in MTD has focused on enhancing its effectiveness by combining it with cyber deception techniques. However, there is limited research on evaluating and quantifying this hybrid defence framework. Existing studies on MTD evaluation often overlook the deployment of deception, which can expand the potential attack surface and introduce additional costs. Moreover, a unified model that simultaneously measures security, reliability, and defense cost is lacking. We propose a novel hybrid defense effectiveness evaluation method that integrates queuing and evolutionary game theories to tackle these challenges. The proposed method quantifies the safety, reliability, and defense cost. Additionally, we construct an evolutionary game model of MTD and deception, jointly optimizing triggering and deployment strategies to minimize the attack success rate. Furthermore, we introduce a hybrid strategy selection algorithm to evaluate the impact of various strategy combinations on security, resource consumption, and availability. Simulation and experimental results demonstrate that the proposed approach can accurately evaluate and guide the configuration of hybrid defenses. Demonstrating that hybrid defense can effectively reduce the attack success rate and unnecessary overhead while maintaining Quality of Service (QoS). Full article

Traditional cyber attack-defense strategies have traditionally focused solely on the attacker and defender, while neglecting the role of government-led system administrators. To address strategic selection challenges in cyber warfare, this study employs an evolutionary game theory framework to construct a tripartite game model involving cyber attackers, defenders, and system administrators. The replicator dynamic equation is utilized for stability analysis of behavioral strategies across stakeholders, with Lyapunov theory applied to evaluate the equilibrium points of pure strategies within the system. MATLAB (2021a) simulations were conducted to validate theoretical findings. Experimental results demonstrate that the model achieves evolutionary stability under various scenarios, yielding optimal defense strategies that provide theoretical support for addressing cybersecurity challenges. Full article

Aiming at the problem that it is difficult to balance security defense and quality of service in a dynamic cloud-native environment, an adaptive dynamic defense strategy (AD2S) for microservices based on deep reinforcement learning is proposed. First, a microservice attack graph model is constructed to extract security threats from multiple dimensions. Combined with queuing theory, the relationships among security performance, quality of service, cleaning cycle, and replica quantity are established to quantitatively model the effectiveness of defense. Subsequently, an adaptive defense framework is designed, which includes state monitoring, policy deployment, and optimization algorithms based on deep reinforcement learning, providing a rapid update solution for the optimal system configuration of microservices under dynamic traffic requests. The experimental results show that under dynamic traffic requests, compared with the existing DSEOM and OADSF strategies, AD2S improves the defense effectiveness by 34.38% and 10.29%, respectively, while ensuring the quality of service, significantly enhancing the system’s security adaptive ability. Full article

The study of attack–defense game decision making in critical infrastructure systems confronting intelligent adversaries, grounded in complex network theory, has emerged as a prominent topic in the field of network security. Most existing research centers on game-theoretic analysis under conditions of complete information and assumes that the attacker and defender share congruent criteria for evaluating target values. However, in reality, asymmetric value perception may lead to different evaluation criteria for both the offensive and defensive sides. This paper examines the game problem wherein the attacker and defender possess distinct target value evaluation criteria. The research findings reveal that both the attacker and defender have their own “advantage ranges” for value assessment, and topological heterogeneity is the reason for this phenomenon. Within their respective advantage ranges, the attacker or defender can adopt clear-cut strategies to secure optimal benefits—without needing to consider their opponents’ decisions. Outside these ranges, we explore how the attacker can leverage small-sample detection outcomes to probabilistically infer defenders’ strategies, and we further analyze the attackers’ preference strategy selections under varying acceptable security thresholds and penalty coefficients. The research results deliver more practical solutions for games involving uncertain value criteria. Full article

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article

The interconnectivity of avionics systems supports the need to incorporate functional safety and information security into airworthiness validation and maintenance protocols, which is critical. This necessity arises from the demanding operational environments and the limitations on defense resource allocation. This study proposes an optimization model for the strategic deployment of defense mechanisms, leveraging the dynamic interplay between attack and defense modeled by non-cooperative game theory and aligning with the maintenance schedules of civil aircraft. By developing an Attack–Defense Tree and conducting a non-cooperative game analysis, this paper outlines strategies from both the attacker’s and defender’s perspectives, assessing the impact of focused defense improvements on the system’s security integrity. The results reveal that the broad expansion of defense measures reduces their effectiveness, whereas targeted deployment significantly enhances protection. Monte Carlo simulations are employed to approximate equilibrium solutions across the strategy space, reducing computational complexity while retaining robustness in capturing equilibrium trends. This approach supports efficient allocation of defense resources, strengthens overall system security, and provides a practical foundation for integrating security analysis into avionics maintenance and certification processes. Full article

Low-Altitude and Slow-Speed Small (LSS) targets pose significant challenges to air defense systems due to their low detectability and complex maneuverability. To enhance defense capabilities against low-altitude targets and assist in formulating interception decisions, this study proposes a new threat assessment algorithm based on multisource data fusion under visible-light detection conditions. Firstly, threat assessment indicators and their membership functions are defined to characterize LSS targets, and a comprehensive evaluation system is established. To reduce the impact of uncertainties in weight allocation on the threat assessment results, a combined weighting method based on bias coefficients is proposed. The proposed weighting method integrates the analytic hierarchy process (AHP), entropy weighting, and CRITIC methods to optimize the fusion of subjective and objective weights. Subsequently, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Dempster–Shafer (D-S) evidence theory are used to calculate and rank the target threat levels so as to reduce conflicts and uncertainties from heterogeneous data sources. Finally, the effectiveness and reliability of the two methods are verified through simulation experiments and measured data. The experimental results show that the TOPSIS method can significantly discriminate threat values, making it suitable for environments requiring rapid distinction between high- and low-threat targets. The D-S evidence theory, on the other hand, has strong anti-interference capability, making it suitable for environments requiring a balance between subjective and objective uncertainties. Both methods can improve the reliability of threat assessment in complex environments, providing valuable support for air defense command and control systems. Full article