Search Results (180)

In highly dynamic vehicular ad hoc networks (VANETs), vehicles frequently move across the coverage areas of multiple roadside units (RSUs), making secure and efficient continuous vehicle-to-infrastructure access essential. However, repeated full authentication and key agreement for each new RSU access impose considerable computational and communication overhead. This paper proposes a state-assisted privacy-preserving mutual authentication and key agreement scheme for lightweight multi-RSU access in VANETs. The proposed scheme consists of initial and subsequent authentication phases. In the initial phase, elliptic curve cryptography (ECC) is used to achieve anonymous mutual authentication and session key establishment between vehicles and RSUs. In the subsequent authentication phase, a vehicle leverages follow-up authentication state securely forwarded by the previous RSU to complete fast authentication with a neighboring RSU using only hash and XOR operations. In addition, physically unclonable functions (PUFs) are deployed on both vehicles and RSUs to protect critical secrets. Security analysis shows that the proposed scheme achieves mutual authentication, anonymity preservation, and resistance to common attacks. Performance evaluation shows that it reduces the computational cost of subsequent authentication by more than 90% while maintaining low communication overhead. Full article

The rapid growth of Intelligent Transportation Systems (ITSs) necessitates secure and efficient Vehicle-to-Everything (V2X) communication. However, existing Physical Unclonable Function (PUF)-based schemes often suffer from modeling vulnerabilities and high overheads. This paper proposes a decentralized, dynamic, anonymous authentication protocol tailored for Vehicular Ad Hoc Networks (VANETs). By integrating Elliptic Curve Cryptography (ECC) with highly reliable Self-Adaption Deviation Locking PUFs (SDL PUFs), we design a dynamic Challenge–Response Pair (CRP) obfuscation mechanism. This mechanism effectively mitigates modeling threats, reducing the prediction success rate of machine learning (ML) and deep learning (DL) attacks by approximately 35% compared to raw SDL PUFs. The protocol ensures identity untraceability and forward secrecy through anonymous identifiers and ephemeral session keys. Security is formally verified under the Real-or-Random (ROR) model and validated using the AVISPA tool. Simulations in SUMO and Omnetpp demonstrate that the protocol is highly efficient, achieving a low computational overhead of 6.77 ms per entity and a communication cost of 192 bytes. Compared to state-of-the-art approaches, our solution provides superior robustness against advanced modeling attacks and significantly reduces latency, making it suitable for resource-constrained V2X environments. Full article

In emerging environments such as cloud computing and the Internet of Things (IoT), secure authentication and key negotiation play a crucial role in protecting data transmitted over public networks. However, many existing authentication protocols are still designed based on classical public-key cryptography primitives, and quantum computing may threaten their security. To address this challenge, we propose a post-quantum authentication and key agreement protocol that uses the lattice-based Kyber key encapsulation mechanism (KEM). Our proposed protocol integrates cryptographic authentication, smart card protection, and post-quantum key encapsulation mechanisms, enabling mutual authentication between users and servers and securely establishing session keys. The security of the protocol is formally analyzed in the Real-or-Random (ROR) model under the random oracle assumption and the IND-CCA security of the underlying KEM scheme. Furthermore, through informal security analysis, we have further demonstrated that the protocol possesses important security properties, including anonymity, untraceability, perfect forward confidentiality, and resistance to known attacks. In addition, the computational cost and communication overhead of the proposed scheme are evaluated and compared with several representative authentication protocols. The results show that the proposed protocol can provide strong security while maintaining low computational cost and communication overhead. Full article

Tor is an anonymization network that enables access to hidden services and protects user identity through layered encryption. While its core technology offers strong privacy, users can still be exposed through indirect attack methods or configuration mistakes. This research not only explores de-anonymization techniques but also provides a practical guide for constructing a fully functional experimental Tor environment using virtual machines. The custom-built testbed allows for safe simulation of attacks without impacting the public Tor network. Within this environment, three key information-gathering approaches were evaluated: (1) malware-based reverse shells that establish external communication, (2) malicious PDF and Office files used to trigger outbound connections, and (3) analysis of service misconfigurations that may reveal the IP address of hidden services. The results confirm that although the Tor network itself is resilient, user behavior, improper configurations, and insecure content handling can lead to significant privacy risks. By combining practical environment setup with real-world attack scenarios, this paper serves both as a reference for building experimental Tor networks and as a security-oriented analysis of known de-anonymization vectors. The findings emphasize the critical need for user awareness and precise configuration in privacy-focused technologies. Full article

In current mobile networks, users’ identity privacy is threatened by long-term observation attacks. To resist such attacks, identity-anonymity technology has been proposed. However, existing anonymity schemes cannot adapt to diverse, dynamic business scenarios because of their rigid anonymity strategies. This leads to wasted computing and communication resources in low-risk scenarios or privacy leaks in high-risk scenarios. To address this problem, we propose an Adaptive Dynamic Anonymity and Traceability scheme based on privacy-aware random forest and local differential privacy in a Trusted Execution Environment. We first construct a convex optimization model to seek the optimal balance between privacy risk and performance cost. Subsequently, we train a privacy-aware random forest model to intelligently predict the optimal Time-To-Live of the anonymous identifier based on the real-time context. Lastly, to resist long-term observation attacks, our scheme uses a lightweight symmetric encryption algorithm to generate pseudo-random, anonymous identifiers and applies truncated local differential privacy to ensure the indistinguishability of the timing patterns of anonymous identifier updates. We formally prove that our scheme can resist long-term observation attacks. Experimental results show that, compared with fixed Time-To-Live schemes, our scheme significantly reduces the comprehensive cost while maintaining the same level of security. Furthermore, compared with traditional public-key schemes, it greatly improves the generation speed of anonymous identifiers and reduces communication costs. Full article

Anonymous communication networks such as the Invisible Internet Project (I2P) are essential for safeguarding privacy and ensuring freedom of expression, necessitating robust performance and security evaluation in controlled environments. Network testbeds offer a reliable alternative to real-world testing. This paper proposes a dynamic modeling framework based on Graph Attention Network (GAT). We introduce a Region-Centric Initialization (RCI) strategy to establish an initial observation anchor, followed by a GAT-based Locality-Aware (GAT-LA) sampling mechanism that treats representative node selection as a dynamic learning task. Experimental results demonstrate that the GAT-LA mechanism significantly outperforms static methods in maintaining long-term similarity to real-world I2P performance metrics. The integrated stability penalty mechanism effectively suppresses excessive topological fluctuations, ensuring temporal smoothness across evolutionary cycles. Furthermore, the RCI strategy provides high engineering flexibility by supporting both automated scoring and target-oriented manual configuration. This paper presents a scalable methodology for dynamic network simulation with enhanced statistical alignment, providing a practical reference for security research within resource-constrained anonymous network ranges or testbeds. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

This qualitative, longitudinal study explores how student members’ sense of belonging changed over time in a Networked Improvement Community (NIC). Co-authored by researchers and student participants, this study uses the “students as partners” framework and four constructs of belonging (motivation, competencies, opportunities, and perceptions) to examine how sense of belonging manifested and developed for five student NIC members across their first and final semesters of participation in the NIC. Retrospective analysis of journal entries and interview data collected over two years revealed: student motivations shifted from individual goals to a collective investment in the NIC’s systemic improvement goals; students developed competencies in leadership, data-informed decision-making, and equity based on their lived experiences; and intentional NIC structures supported a shift in the student members’ opportunities for belonging from relationship-building and anonymous decision-making to taking on more responsibilities in NIC initiatives. As a result, student perceptions evolved from uncertainty about their roles to having a strong sense of agency, influence, and community within the department. The findings from this study offer evidence that intentionally structured NICs can function as spaces where motivations, competencies, opportunities, and perceptions grow together and position student members as contributors to inclusive systemic change in undergraduate STEM education. Full article

To address the issues of insufficient privacy protection, lack of confidentiality, and absence of traceability mechanisms in resource-constrained application scenarios such as IoT nodes or mobile network group communications, this paper proposes a traceable ring signcryption privacy protection scheme based on the SM9 algorithm. In detail, the ring signcryption structure is designed based on the SM9 identity-based cryptography algorithm framework. Additionally, the scheme introduces a dynamic accumulator to compress ciphertext length and optimizes the algorithm to improve computational efficiency. Under the random oracle model, it is proved that the scheme has unforgeability, confidentiality, and conditional anonymity, and it is also demonstrated that conditional anonymity can be used to trace the identity of the actual signcryptor in the event of a dispute. Performance analysis shows that, compared with related schemes, this scheme improves the efficiency of signcryption, and the size of the signcryption ciphertext remains at a constant level. Full article

The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive data across heterogeneous and resource-constrained networks. This review explores the current landscape of privacy-preserving protocols designed for Smart City and IIoT infrastructures. We examine state-of-the-art approaches including lightweight cryptographic schemes, secure data aggregation, anonymous communication protocols, and blockchain-based frameworks. The paper also analyzes practical trade-offs between security, latency, and computational overhead in real-world deployments. Open research challenges such as secure interoperability, privacy in federated learning, and resilience against AI-driven cyberattacks are discussed. Finally, the paper outlines promising research directions and technologies that can enable scalable, secure, and privacy-aware network infrastructures for future urban and industrial ecosystems. Full article

Wireless medical sensor networks (WMSNs) have evolved alongside the development of communication systems, and the integration of cloud computing has enabled scalable and efficient medical data management. However, since the messages in WMSNs are transmitted over open channels, they are vulnerable to eavesdropping, replay, impersonation, and other various attacks. In response to these security concerns, Keshta et al. suggested an authentication protocol to establish secure communication in the cloud-assisted WMSNs. However, our analysis reveals their protocol cannot prevent session key disclosure, impersonation of the user and sensor node, and denial of service (DoS) attacks. Moreover, Keshta et al.’s protocol cannot support user untraceability due to fixed hidden identity. To address these weaknesses, we propose a physical unclonable function (PUF) based secure authentication protocol for cloud-assisted WMSNs. The protocol uses lightweight operations, provides mutual authentication between user, cloud server, and sensor node, and supports user anonymity and untraceability. We validate the proposed protocol’s security through informal analysis on various security attacks and formal analysis including “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (RoR) model” for session key security, and “Automated Validation of Internet Security Protocols and Application (AVISPA) simulations”. Performance evaluation demonstrates lower communication cost and computation overhead compared with existing protocols, making the proposed protocol suitable for WMSN environments. Full article

Across a diverse landscape of anonymity designs, the dominant paradigms—onion routing (e.g., Tor) and mix networks (e.g., Loopix)—carry intrinsic constraints: long-lived circuits invite traffic correlation, and mixnets often rely on a network-wide state, making it hard to reconcile anonymity and scalability. This paper presents Draughts, a fully decentralized system in which each packet follows an independent and dynamically determined transmission path. Built upon Jump routing, Draughts introduces three key innovations: (i) replacing global state $O\left(N\right)$ with local two-hop neighborhood knowledge $O\left(k^2\right)$; (ii) supporting anonymous replies to enable real-time bidirectional communication; and (iii) proposing a path-length control mechanism that balances anonymity and transmission efficiency. Evaluation results show that Draughts achieves strong sender anonymity, resists predecessor and traffic analysis attacks, and reduces receiver buffer maintenance overhead, achieving a favorable trade-off between anonymity and performance. Full article

Accommodation constitutes a key element in the competitiveness of a destination. However, there is limited information on how overnight tourists evaluate the destination according to the type of accommodation. To date, Information and Communication Technologies (ICTs) have led to an increased use of online media and the generation of large datasets available for analysis. This study analyses 47,568 feedback surveys administered by Spanish accommodation establishments to tourists, provided anonymously by a tourism software company. The main objective is to understand which destination attributes predict positive results on satisfaction and recommendation, depending on the type of accommodation (hotel and non-hotel). To this end, both a descriptive analysis and an analysis using neural networks are conducted. The results reveal significant differences in the evaluation of destination elements depending on whether the accommodation is a hotel or non-hotel, with the predictive variables varying across each typology. As a key conclusion, the study highlights that adopting this perspective makes it possible to understand destination evaluation from the standpoint of overnight tourists, thereby contributing to destination-related literature. From a practical perspective, recommendations are offered to both destination managers and accommodation providers. Full article

Dark web forums are critical platforms for illicit activities and anonymous communication, making their analysis essential for cybersecurity, law enforcement, and academic research. This systematic literature review synthesises methodologies for data collection and analysis of dark web forum content. Following PRISMA 2020 guidelines, we searched SciSpace, Google Scholar, and PubMed, identifying 364 papers, of which 11 provided detailed methodological insights. Key methodologies include web crawling, machine learning, natural language processing, and social network analysis. Results show the dominance of Python-based automated tools, with hybrid approaches combining automation and manual verification proving most effective. Challenges include ethical considerations, data accessibility, and platform dynamism. The field is maturing but requires standardised frameworks and improved reproducibility. This review outlines current practices, evaluates methodological effectiveness, and suggests future directions for research and application. Full article

Secure authentication in vehicular ad hoc networks (VANETs) remains a fundamental challenge due to their dynamic topology, susceptibility to attacks, and scalability constraints in multi-hop communication. Existing approaches based on elliptic curve cryptography (ECC), blockchain, and fog computing have achieved partial success but suffer from latency, resource overhead, and limited adaptability, leaving a gap for lightweight and hardware-rooted trust models. To address this, we propose a multi-hop mutual authentication protocol leveraging Physical Unclonable Functions (PUFs), which provide tamper-evident, device-specific responses for cryptographic key generation. Our design introduces a structured sequence of phases, including pre-deployment, registration, login, authentication, key establishment, and session maintenance, with optional multi-hop extension through relay vehicles. Unlike prior schemes, our protocol integrates fuzzy extractors for error tolerance, employs both inductive and game-based proofs for security guarantees, and maps BAN-logic reasoning to specific attack resistances, ensuring robustness against replay, impersonation, and man-in-the-middle attacks. The protocol achieves mutual trust between vehicles and RSUs while preserving anonymity via temporary identifiers and achieving forward secrecy through non-reused CRPs. Conceptual comparison with state-of-the-art PUF-based and non-PUF schemes highlights the potential for reduced latency, lower communication overhead, and improved scalability via cloud-assisted CRP lifecycle management, while pointing to the need for future empirical validation through simulation and prototyping. This work not only provides a secure and efficient solution for VANET authentication but also advances the field by offering the first integrated taxonomy-driven evaluation of PUF-enabled V2X protocols in multi-hop Wi-Fi environments. Full article