Search Results (152)

Delivery platforms in urban logistics connect providers with customers through distribution riders, who are usually distinguished by low incomes and limited social rights. This paper aims to compare and analyze the freelance and employment models for riders in different European countries in terms of social sustainability, i.e., work motivation and labor rights. To reach this goal, two activities were performed. On the one hand, qualitative interviews with German and Italian riders were carried out. On the other hand, a dynamic metaheuristic algorithm was developed and implemented to simulate an employment model with a central provider that manages order requests in real-time. The qualitative interviews indicate that riders’ motivations differ between freelance riders and employed riders: freelance riders do feel more controlled. Using a quantitative algorithm, this manuscript shows that when an efficient centralized order–rider assignment strategy is applied, a socially sustainable and simultaneously profitable employment model for food delivery businesses is possible. The results have the potential to legitimize adequate rights and salaries for riders while allowing digital platforms to operate profitably. Such win–win situations could support the implementation of platform structures across different logistics sectors and overcome conflicts regarding working rights in such contexts. 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 4th and 5th centuries marked a pivotal phase in the development of the Goguryeo regime. Its tomb murals epitomize the visual strategies of state-building, serving to establish a “sacred theater” of power. Taking Tomb No. 4 of the Wukui complex as a case in point, the murals reveal localized adaptations of the Fuxi–Nüwa imagery, blending the Central Plains’ sun-deity worship with Goguryeo’s ancestral mythology through the symbol of the sun-centered Three-Legged Crow, thereby legitimizing the sacred lineage of royal authority. The function of the Four Symbols (Sishen) imagery evolved from mere directional markers into guardians of sovereignty, reflecting deeper cultural transformations. The diachronic evolution of mural themes traces the trajectory of political change: in the 4th century, murals centered on wrestling and banqueting scenes, reinforcing ethnic identity and consolidating tribal alliances through ritualized displays of strength and hierarchical banquet etiquette. By the 5th century, the themes shifted to hunting, processions, and Buddhist rituals, where military metaphors and ceremonial norms underscored the rise of a centralized bureaucratic system and the imperatives of territorial expansion. Through three interlocking mechanisms—symbolic reconfiguration, spatial narrative, and sensory manipulation—Goguryeo tomb murals constructed a closed value system linking worldly authority to posthumous order, serving as material testimony to the enduring “covenant between humans and deities.” Full article

The growing influence of social media on political processes extends beyond electoral campaigns and is rapidly transforming the communication practices of incumbent leaders. We address the gap between populist practices in electoral marketing and the implementation of the Ecuadorian president’s discursive strategies from a geopolitical perspective, with a special focus on the use of two platforms: Instagram and TikTok. While existing scholarship has generally analyzed populist discourse on social media, this article applies theoretical and methodological tools to analyze the grammar of war and the performative strategies used to build leadership in contexts of high social unrest. Grounded in contemporary perspectives. This article reveals how populist leaders mobilize emotions through narratives on digital platforms to frame political crises. Using qualitative critical discourse analysis with multimodal and semiotic tools, we examined 156 posts from the official TikTok and Instagram accounts of Ecuadorian President Daniel Noboa, published between January and July 2024. The findings highlight the strategic use of patriotic symbolism, personalization, and emotional appeals to legitimize executive actions and disseminate polarizing narratives. The proposed framework demonstrates how social media communication simplifies complex crisis scenarios into affect-laden “good versus evil” narratives. This model is transferable to other geopolitical and digital contexts, offering both conceptual and methodological tools for analyzing conflict-driven political communication. Full article

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length ($lp$) and packet inter-arrival time ($\mathrm{\Delta }t$). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus. Full article

This study models how designable cues in digital heritage promotion shape advocacy through affect and memory. Relying on the stimulus–organism–response paradigm, we argue that three stimuli, Visual Sensory Appeal (VSA), Narrative Immersion (NI), and Perceived Authenticity (PA), trigger Emotional Engagement (EE) and become Destination Memory (DM), leading to Intention to Recommend (IR). A cross-sectional quantitative design with an online self-report survey was employed. Using Structural Equation Modeling (SEM) we modeled 653 usable responses to test hypothesized stimulus–organism–response processes and Multi-Group Analysis (MGA) tested heterogeneity across gender, age, education, recent contact, cultural-travel frequency, preservation interest, prior heritage experience, and technology use. Direct associations revealed VSA was a strong predictor of IR, and EE and DM predicted IR positively. NI and PA were not incrementally directly affecting IR. Mediation tests revealed partial mediation for VSA (through EE and DM) and complete mediation for NI and PA; across all stimuli, DM far surpassed EE, suggesting memory consolidation as the overall mechanism. MGA revealed systematic segmentation: women preferred visual and authenticity approaches; men used affective conversion, narrative, and authenticity-to-memory more; young adults preferred story/memory levers; higher education made authenticity pathways legitimate; exposure, experience, sustainability interest, and technology use further conditioned strength of paths. Results sharpen S–O–R accounts by ranking visual design as a proximal driver and placing EE on DM as the central channel through which narrative and authenticity have their influence. In practice, the research supports visually consistent, memory-backed, segment-specific strategies for sustainable, inclusive heritage communication. Full article

This article examines how populist radical right parties (PRR) in three contrasting European contexts—Slovenia, France, and Poland—strategically instrumentalize Christianity within their anti-immigration agendas. Rather than using religion as a matter of faith, these parties recast Christianity as a cornerstone of national and European identity, positioning it in opposition to Islam and non-European migration. The study argues that such instrumentalization serves not only to construct a religiously defined national identity, but also to legitimize exclusionary policies. By analyzing selected political speeches, party manifestos, and media discourse, we explore how far-right actors frame Islam as incompatible with European values, reinforcing the division between “Christian Europe” and “foreign non-Christian migrants.” Drawing on recent scholarship on civilizational populism and religious boundary-making, we further assess how processes of globalization and European integration have been interpreted by populist parties to fuel anti-immigrant sentiment. Methodologically, we employ qualitative content analysis to identify recurring themes and rhetorical strategies, with a focus on the intersection of religion, nationalism, and migration. The findings contribute to debates on religious pluralism in contemporary Europe, shedding light on how far-right populism reframes pluralism and challenges secular principles across different political and cultural settings. Full article

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments. Full article

In complex urban wireless environments, unmanned aerial vehicle–mobile edge computing (UAV-MEC) systems face challenges like link blockage and single-antenna eavesdropping threats. The traditional single reconfigurable intelligent surface (RIS), limited in collaboration, struggles to address these issues. This paper proposes a double-RIS cooperative UAV-MEC optimization scheme, leveraging their joint reflection to build multi-dimensional signal paths, boosting legitimate link gains while suppressing eavesdropping channels. It considers double-RIS phase shifts, ground user (GU) transmission power, UAV trajectories, resource allocation, and receiving beamforming, aiming to maximize secure energy efficiency (EE) while ensuring long-term stability of GU and UAV task queues. Given random task arrivals and high-dimensional variable coupling, a dynamic model integrating queue stability and secure transmission constraints is built using Lyapunov optimization, transforming long-term stochastic optimization into slot-by-slot deterministic decisions via the drift-plus-penalty method. To handle high-dimensional continuous spaces, an end-to-end proximal policy optimization (PPO) framework is designed for online learning of multi-dimensional resource allocation and direct acquisition of joint optimization strategies. Simulation results show that compared with benchmark schemes (e.g., single RIS, non-cooperative double RIS) and reinforcement learning algorithms (e.g., advantage actor–critic (A2C), deep deterministic policy gradient (DDPG), deep Q-network (DQN)), the proposed scheme achieves significant improvements in secure EE and queue stability, with faster convergence and better optimization effects, fully verifying its superiority and robustness in complex scenarios. Full article

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Radio Frequency Identification (RFID) technology is widely applied in various scenarios, including logistics tracking, supply chain management, and target monitoring. In these contexts, the malicious cloning of legitimate tag information can lead to sensitive data leakage and disrupt the normal acquisition of tag information by readers, thereby threatening personal privacy and corporate security and incurring significant economic losses. Although some efforts have been made to detect cloning attacks, the presence of missing tags in RFID systems can obscure cloned ones, resulting in a significant reduction in identification efficiency and accuracy. To address these problems, we propose the block-based cloned tag identification (BCTI) protocol for identifying cloning attacks in the presence of missing tags. First, we introduce a block indicator to sort all tags systematically and design a block mechanism that enables tags to respond repeatedly within a block with minimal time overhead. Then, we design a superposition strategy to further reduce the number of verification times, thereby decreasing the execution overhead. Through an in-depth analysis of potential tag response patterns, we develop a precise method to identify cloning attacks and mitigate interference from missing tags in probabilistic cloning attack scenarios. Moreover, we perform parameter optimization of the BCTI protocol and validate its performance across diverse operational scenarios. Extensive simulation results demonstrate that the BCTI protocol meets the required identification reliability threshold and achieves an average improvement of $24.01\%$ in identification efficiency compared to state-of-the-art solutions. Full article

This study introduces a novel framework for fair resource allocation in self-governing, multi-agent systems, leveraging principles of interactional justice to enable agents to autonomously evaluate fairness in both individual and collective resource distribution. Central to our approach is the integration of Rescher’s canons of distributive justice, which provide a comprehensive, multidimensional framework encompassing equality, need, effort and productivity to assess legitimate claims on resources. In resource-constrained environments, multiagent systems require a balance between fairness and satisfaction. We compare the Borda Count (BC) method with K-means clustering, which group agents by similarity and allocate resources based on cluster averages. According to our findings, the BC method effectively prioritized the highest needs of the agents and resulted in higher satisfaction. On the other hand, K-means achieved higher fairness and facilitated a more equitable distribution of resources. The study showed that there was an intrinsic balance between fairness and satisfaction with the allocation of resources. The BC method is more suitable when individual needs are the main concern, while K-means is better when ensuring an equitable distribution between agents. In this work, we provide a refined understanding of the resource allocation strategies of multi-agent systems and emphasize the strengths and limitations of each approach to help system designers choose the appropriate methods. Full article

This article aims to evaluate the runtime effectiveness of various one-class classification (OCC) techniques for anomaly detection in an industrial scenario reproduced in a laboratory setting. To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. After analyzing major publicly available datasets, such as KDD Cup 1999 and TON-IoT, as well as the most widely used OCC techniques, a lightweight and modular intrusion detection system (IDS) was developed in Python. The system was tested in real time on an experimental platform based on Raspberry Pi, within a simulated client–server environment using the NFSv4 protocol over TCP/UDP. Several OCC models were compared, including One-Class SVM, Autoencoder, VAE, and Isolation Forest. The results showed strong performance in terms of detection accuracy and low latency, with the best outcomes achieved using the UNSW-NB15 dataset. The article concludes with a discussion of additional strategies to enhance the runtime analysis of these algorithms, offering insights into potential future applications and improvement directions. Full article

Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of surveillance by sending interference signals to suspicious receivers, which is important for crime prevention and public safety. In this paper, we investigate the joint optimization of performance of both ISAC and active surveillance. Specifically, we formulate a bilevel optimization problem where the upper-level objective aims to maximize the probability of successful eavesdropping while the lower-level objective aims to optimize the localization performance of the radar on suspicious transmitters. By employing the Rayleigh quotient, introducing a decoupling strategy, and adding penalty terms, we propose an algorithm to solve the bilevel problem where the lower-level objective is convex. With the help of the proposed algorithm, we obtain the optimal solution of the analog transmit beamforming matrix and the digital beamforming vector. Performance analysis and discussion of key insights, such as the trade-off between eavesdropping success probability and radar localization accuracy, are also provided. Finally, comprehensive simulation results validate the effectiveness of our proposed algorithm in enhancing both the eavesdropping success probability and the accuracy of radar localization. Full article

Indonesia’s post-mining landscapes require an integrated governance approach to achieve equitable and sustainable reclamation. This study developed and evaluated the TILANG Framework (Triple-Bottom-Line Integrated Land Governance) as a multidimensional model that aligns ecological restoration, community empowerment, and institutional accountability. Based on a meta-synthesis of 773 academic and institutional remarks coded using NVivo 12, the study identified sustainable cacao agriculture as a viable compensation mechanism that supports livelihood recovery while restoring degraded land. The framework draws on six foundational theoretical components—Corporate Social Responsibility (CSR), Stakeholder Theory, Legitimacy Theory, the Theory of Planned Behavior, the Triple Bottom Line, and multi-level governance—and is operationalized through six implementation principles: Trust, Inclusivity, Legitimacy, Alignment, Norms, and Governance. The findings support performance-based land reclamation by embedding behavioral readiness and institutional co-financing into sustainability strategies. This model is particularly relevant to Indonesia’s ongoing land-use transformation, where post-extractive zones are shifting toward agroecological and community-centered recovery. The study found that (1) reframing land compensation as a restorative, performance-based mechanism enables more legitimate and inclusive post-mining governance; (2) sustainable cacao agriculture represents a viable and socially accepted strategy for ecological recovery and rural livelihood revitalization; and (3) the TILANG Framework advances land-use transformation by integrating corporate responsibility, behavioral readiness, and multi-level governance into a cohesive performance model. Full article