Search Results (98)

The security of financial messaging systems is critical to maintaining trust in digital financial platforms. Despite advances in cryptography, many contemporary systems remain vulnerable to channel-based and cryptographic threats, including eavesdropping, interception, tampering, and unauthorized access. Hybrid cryptographic models that combine asymmetric encryption for secure key exchange with symmetric encryption for efficient data protection have emerged as effective approaches for strengthening confidentiality, integrity, and authenticity in financial message communications. This study presents a scoping review of literature published between 2015 and 2025, mapping research on user vulnerabilities in financial messaging systems and examining the role of hybrid cryptographic models in mitigating these risks. Guided by the PRISMA-ScR reporting standards, 615 articles were identified across nine scholarly databases. Forty-four studies met the inclusion criteria after systematic screening. The findings reveal a growing emphasis on hybrid encryption strategies, particularly RSA–AES and ECC–AES combinations, due to their balance of security strength and computational efficiency. However, significant gaps persist in empirical validation, real-world deployment, and user-centred security design, especially in mobile-first and resource-constrained environments. Existing research largely prioritizes theoretical performance and algorithmic efficiency, with limited attention to practical integration, usability, and operational constraints. This review highlights the need for holistic security frameworks that integrate cryptographic robustness with usability, regulatory compliance, and contextual deployment considerations. It provides a structured foundation for future research focused on developing scalable, user-centric, and resilient security solutions for financial messaging systems. Full article

Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols induces unnecessary CPU utilization. In this study, we repurpose Slowloris as an energy-oriented (E-DoS) attack that exploits pre-authentication statefulness of the most prevalent remote access protocols, the Secure Shell Protocol (SSH) and File Transfer Protocol (FTP). We employ a Raspberry Pi-based experimental setup with different software implementations of the mentioned protocols to validate our hypothesis. Our experiments confirm the susceptibility of SSH and FTP to Slowloris E-DoS attacks, and we quantify the consequential impact on power consumption. We find that the Slowloris E-DoS attack exhibits an asymmetrical nature, causing a disproportionate computational demand on victim systems compared to the resources invested by the attacker. The results of this study indicate that battery-powered single-board computers (SBCs) are critically affected by these attacks due to their limited power availability. This research demonstrates the importance of understanding and mitigating Slowloris E-DoS vulnerabilities in the SSH and FTP protocols, offering valuable insights for enhancing security measures. Our findings show that millions of SBCs worldwide may be at risk and highlight a deeper structural weakness: the stateful design of widely deployed protocols can turn service availability into an energy liability. This systemic risk extends beyond SSH and FTP, with implications for IoT devices and backends that depend on stateful communication protocols. Full article

During elections, information manipulation on social media has accelerated the use of artificial intelligence, yet the evidence is difficult to interpret without an integrated view of methods, data, and evaluation. We mapped 557 English-language journal articles from Scopus and Web of Science, combining performance indicators, science mapping, and a focused full-text synthesis of highly cited papers. The literature grows sharply after 2019, peaks in 2025, and shows geographically uneven production, with collaboration structured around a small set of hubs. The thematic structure suggests that, during the pandemic era, infodemic-related research served as a catalyst, intensifying scientific attention to fake news and disinformation and expanding the associated detection and monitoring agendas. In addition, socio-political harm constructs such as hate speech, extremism, and polarization appear as recurrent and structurally central targets, highlighting that election-relevant work often extends beyond veracity assessment toward monitoring discourse risks. Blockchain also emerges as a novel and adjacent integrity theme, aligned with authenticity and provenance-oriented mitigation rather than mainstream detection pipelines. AI for electoral disinformation is not reducible to veracity classification, as influential studies also target automation and coordinated behavior, verification support, diffusion analysis, and estimation frameworks that focus on exposure and impact. Evaluation remains heterogeneous and is often shaped by benchmark settings, making high accuracy values hard to compare and potentially misleading when labeling quality, topic leakage, or context shift are not characterized. Overall, the findings motivate evaluation protocols that align operational objectives with modeling roles and explicitly address robustness to temporal and platform changes, asymmetric error costs during election windows, and representativeness across electoral contexts and languages, while also guiding future work on emerging integrity challenges and governance-relevant deployment settings. Full article

Micro-scale waterfront spaces play a critical role in contemporary urban regeneration by supporting everyday activities and place-based experiences. However, existing studies often rely on linear evaluation approaches and insufficiently address the asymmetric effects of functional, environmental, and cultural attributes on residents’ landscape satisfaction. This study investigates the satisfaction structure of micro-scale waterfront spaces along the Grand Canal in Wuxi, China, with a particular focus on nonlinear demand mechanisms. A mixed-method framework integrating grounded theory, the Delphi method, and the Kano model was employed to identify key landscape attributes and classify their satisfaction effects. The results reveal a hierarchical satisfaction mechanism characterized by “basic–performance–attractive” attributes. Fundamental functional and environmental factors, such as accessibility, safety, water quality, and cultural authenticity, function as must-be attributes that primarily prevent dissatisfaction. Environmental comfort and social facilities act as one-dimensional attributes that linearly enhance satisfaction, while cultural narratives, memory-related elements, and ecological esthetics emerge as attractive attributes that significantly elevate emotional engagement when present. Sensitivity analysis further identifies priority intervention factors with the greatest impact on satisfaction improvement. These findings demonstrate the asymmetric nature of residents’ landscape satisfaction and provide a phased optimization framework for the sustainable regeneration of heritage-based micro-scale waterfront spaces, emphasizing basic reliability, experiential enhancement, and cultural resonance. Full article

Deepfake technology can produce highly realistic manipulated media which pose as significant cybersecurity threats, including fraud, misinformation, and privacy violations. This research proposes a deepfake prevention approach based on symmetric and asymmetric ciphers. Post-quantum asymmetric ciphers were utilized to perform digital signature operations, which offer essential security services, including integrity, authentication, and non-repudiation. Symmetric ciphers were also employed to provide confidentiality and authentication. Unlike classical ciphers that are vulnerable to quantum attacks, this study adopts quantum-resilient ciphers to offer long-term security. The proposed approach enables entities to digitally sign media content before public release on other platforms. End users can subsequently verify the authenticity of content using the public keys of the media creators. To identify the most efficient ciphers to perform cryptography operations required for deepfake prevention, the study explores the implementation of quantum-resilient symmetric and asymmetric ciphers standardized by NIST, including Dilithium, Falcon, SPHINCS+, and Ascon-80pq. Additionally, this research provides comprehensive comparisons between the various classical and post-quantum ciphers in both categories: symmetric and asymmetric. Experimental results revealed that Dilithium-5 and Falcon-512 algorithms outperform other post-quantum ciphers, with a time delay of 2.50 and 251 ms, respectively, for digital signature operations. The Falcon-512 algorithm also demonstrates superior resource efficiency, making it a cost-effective choice for digital signature operations. With respect to symmetric ciphers, Ascon-80pq achieved the lowest time consumption, taking just 0.015 ms to perform encryption and decryption operations. Also, it is a significant option for constrained devices, since it consumes fewer resources compared to standard symmetric ciphers, such as AES. Through comprehensive evaluations and comparisons of various symmetric and asymmetric ciphers, this study serves as a blueprint to identify the most efficient ciphers to perform the cryptography operations necessary for deepfake prevention. Full article

This paper presents a novel architecture for creating light agents embedded on Internet of Things (IoT) devices, specifically addressing challenges such as security, scalability, and adaptability. Despite the increasing adoption of agent-based approaches in IoT systems, security and robustness mechanisms are often treated as external or ad hoc components in many existing solutions. This limits their effectiveness in dynamic environments that transmit sensitive and personal data and are, by nature, potentially untrusted. The proposed architecture applies Pyro4 for efficient communication among agents and implements a multi-level security scheme that combines symmetric, asymmetric, and hybrid encryption with Time-Based One-Time Passwords (TOTP)-based authentication. This ensures the data confidentiality and integrity within dynamic IoT environments. A case study validates the “agent of things” concept by confirming key security mechanisms such as agent authentication, multi-factor access control, secure communication, and fault resilience. Qualitative testing proved the architecture effective in mitigating common vulnerabilities in distributed agent environments, achieving high reliability scores in terms of security and performance. Experimental results show that over 75% of agent operations were completed in under 2 milliseconds, with a success rate above 99%, confirming the architecture’s lightweight execution and real-time readiness of the architecture for IoT environments. Therefore, the proposed architecture is particularly useful for researchers and practitioners working on secure IoT systems, embedded multi-agent architectures, and intelligent edge computing environments. Full article

Traditional Turkish marbling (Ebru) art is an intangible cultural heritage characterized by highly asymmetric, fluid, and non-reproducible patterns, making its long-term preservation and large-scale dissemination challenging. It is highly sensitive to environmental conditions, making it enormously difficult to mass produce while maintaining its original aesthetic qualities. A data-driven generative model is therefore required to create unlimited, high-fidelity digital surrogates that safeguard this UNESCO heritage against physical loss and enable large-scale cultural applications. This study introduces a deep generative modeling framework for the digital reconstruction of traditional Turkish marbling (Ebru) art using a Deep Convolutional Generative Adversarial Network (DCGAN). A dataset of 20,400 image patches, systematically derived from 17 original marbling works, was used to train the proposed model. The framework aims to mathematically capture the asymmetric, fluid, and stochastic nature of Ebru patterns, enabling the reproduction of their aesthetic structure in a digital medium. The generated images were evaluated using multiple quantitative and perceptual metrics, including Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Learned Perceptual Image Patch Similarity (LPIPS), and PRDC-based indicators (Precision, Recall, Density, Coverage). For experimental validation, the proposed DCGAN framework is additionally compared against a Vanilla GAN baseline trained under identical conditions, highlighting the advantages of convolutional architectures for modeling marbling textures. The results show that the DCGAN model achieved a high level of realism and diversity without mode collapse or overfitting, producing images that were perceptually close to authentic marbling works. In addition to the quantitative evaluation, expert qualitative assessment by a traditional Ebru artist confirmed that the model reproduced the organic textures, color dynamics, and compositional asymmetrical characteristic of real marbling art. The proposed approach demonstrates the potential of deep generative models for the digital preservation, dissemination, and reinterpretation of intangible cultural heritage recognized by UNESCO. Full article

Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability to capture or adapt to the underlying symmetries and asymmetries inherent in real-world wind energy data remains insufficiently explored. In this study, we evaluate and compare these models using authentic production and meteorological data from the Tokat Wind Farm in Türkiye. The forecasting scenarios were designed to reflect the temporal structure of the dataset, including seasonal patterns, recurrent behaviors, and the symmetry-breaking effects caused by abrupt changes in wind speed and operational variability. The results demonstrate that the LSTM model most effectively captures the temporal relationships and partial symmetries within the data, yielding the lowest error metrics (RMSE = 0.2355, MAE = 0.1249, MAPE = 25.16%, R² = 0.8199). GRU and CNN offer moderate performance but show reduced sensitivity to asymmetric fluctuations, particularly during periods of high variability. The comparative findings highlight how symmetry-informed model behavior—specifically the ability to learn repeating temporal structures and respond to symmetry-breaking events—can significantly influence forecasting accuracy. This study provides practical insights into the interplay between data symmetries and model performance, supporting the development of more robust deep learning approaches for real-world wind energy forecasting. Full article

The rapid convergence of the Internet of Things (IoT), quantum computing, and artificial intelligence (AI) has amplified the urgency for lightweight yet resilient data protection mechanisms, particularly within unmanned aerial vehicles (UAV). Traditional cryptographic approaches, while mathematically secure, often fail to reconcile the competing requirements of robustness, computational efficiency, and energy sustainability when deployed on resource-constrained platforms such as drones. To address this gap, this paper proposes a novel hybrid lightweight cryptographic model that strategically integrates symmetric and asymmetric primitives in a dual-layer design. The model leverages the efficiency of lightweight authenticated encryption for high-throughput data protection, while incorporating elliptic-curve and lattice-based key exchange mechanisms to ensure both forward secrecy and post-quantum resilience. Experimental evaluation demonstrates that the proposed scheme achieves superior performance compared to conventional methods, offering reduced computational overhead, lower energy consumption, and enhanced resistance to cyber threats. Crucially, the model maintains high levels of confidentiality, integrity, and authenticity while extending operational endurance, making it particularly well-suited for next-generation UAV operating within the broader IoT ecosystem. Full article

Cyber defense has evolved into an algorithmically intensive discipline where mathematical rigor and adaptive computation underpin the robustness and continuity of digital infrastructures. This review consolidates the algorithmic spectrum that supports modern cyber defense, from cryptographic primitives that ensure confidentiality and integrity to behavioral intelligence algorithms that provide predictive security. Classical symmetric and asymmetric schemes such as AES, ChaCha20, RSA, and ECC define the computational backbone of confidentiality and authentication in current systems. Intrusion and anomaly detection mechanisms range from deterministic pattern matchers exemplified by Aho-Corasick and Boyer-Moore to probabilistic inference models such as Markov Chains and HMMs, as well as deep architectures such as CNNs, RNNs, and Autoencoders. Malware forensics combines graph theory, entropy metrics, and symbolic reasoning into a unified diagnostic framework, while network defense employs graph-theoretic algorithms for routing, flow control, and intrusion propagation. Behavioral paradigms such as reinforcement learning, evolutionary computation, and swarm intelligence transform cyber defense from reactive automation to adaptive cognition. Hybrid architectures now merge deterministic computation with distributed learning and explainable inference to create systems that act, reason, and adapt. This review identifies and contextualizes over 50 foundational algorithms, ranging from AES and RSA to LSTMs, graph-based models, and post-quantum cryptography, and redefines them not as passive utilities, but as the cognitive genome of cyber defense: entities that shape, sustain, and evolve resilience within adversarial environments. Full article

The proliferation of counterfeit products poses a substantial threat to numerous industries. Blockchain technology (BCT) offers an effective solution for product traceability, providing a means to combat counterfeiting. However, BCT can verify the authenticity of the information but cannot confirm the veracity of the product itself, a problem known as counterfeiting at the source. To our knowledge, this issue has yet to be studied. The security level of BCT traceability is used to indicate its ability to combat counterfeiting. We establish game-theoretical models to investigate BCT adoption strategies for a typically authentic firm and a premium firm to fight counterfeiting in a vertically differentiated competition. This study demonstrates that BCT reduces deceptive counterfeiters’ incentive to pool with the branded firm and mitigates the negative impact of asymmetric information on the prices, market share, and profits of authentic products in a monopoly. In instances where the proportion of counterfeits is substantial, premium products will lose market share, a phenomenon often referred to as “bad money driving out good money.” In a vertically differentiated competition, if the quality of the premium product is below a certain threshold, it is recommended that the premium firm be the first to adopt BCT, while the typically authentic firm should not follow (Scenario NB). That is, Scenario NB is a win-win situation for both firms in the competition. The premium firm that has adopted BCT can offer a “free ride” to the typically authentic firm. Full article

With the rapid evolution of smart grids, secure and efficient data communication among hierarchical sensor devices has become critical to ensure privacy and system integrity. However, existing protocols often fail to balance security strength and resource constraints of terminal sensors. In this paper, we propose a novel identity-based secure data communication protocol tailored for hierarchical sensor groups in smart grid environments. The protocol integrates symmetric and asymmetric encryption to enable secure and efficient data sharing. To reduce computational overhead, a Bloom filter is employed for lightweight identity encoding, and a cloud-assisted pre-authentication mechanism is introduced to enhance access efficiency. Furthermore, we design a dynamic group key update scheme with minimal operations to maintain forward and backward security in evolving sensor networks. Security analysis proves that the protocol is resistant to replay and impersonation attacks, while experimental results demonstrate significant improvements in computational and communication efficiency compared to state-of-the-art methods—achieving reductions of 73.94% in authentication computation cost, 37.77% in encryption, and 55.75% in decryption, along with a 79.98% decrease in communication overhead during authentication. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

Multibeam echosounder systems (MBESs) are widely used in unmanned surface vehicle swarms (USVs) to perform various marine bathymetry surveys because of their excellent performance. To address the challenges of systematic error superposition and edge beam error propagation in multibeam bathymetry surveying, this study proposes a novel error adjustment method integrating crossover error density clustering and beam incident angle (BIA) compensation. Firstly, a bathymetry error detection model was developed based on adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN). By optimizing the neighborhood radius and minimum sample threshold through analyzing sliding-window curvature, the method achieved the automatic identification of outliers, reducing crossover discrepancies from ±150 m to ±50 m in the deep sea at a depth of approximately 5000 m. Secondly, an asymmetric quadratic surface correction model was established by incorporating the BIA as a key parameter. A dynamic weight matrix ω = 1/(1 + 0.5θ²) was introduced to suppress edge beam errors, combined with Tikhonov regularization to resolve ill-posed matrix issues. Experimental validation in the Western Pacific demonstrated that the RMSE of crossover points decreased by about 30.4% and the MAE was reduced by 57.3%. The proposed method effectively corrects residual systematic errors while maintaining topographic authenticity, providing a reference for improving the quality of multibeam bathymetric data obtained via USVs and enhancing measurement efficiency. Full article

The Internet of Drones (IoD) overcomes the physical limitations of traditional ground networks with its dynamic topology and 3D spatial flexibility, playing a crucial role in various fields. However, eavesdropping and spoofing attacks in open channel environments threaten data confidentiality and integrity, posing significant challenges to IoD communication. Existing foundational schemes in IoD primarily rely on symmetric cryptography and digital certificates. Symmetric cryptography suffers from key management challenges and static characteristics, making it unsuitable for IoD’s dynamic scenarios. Meanwhile, elliptic curve-based public key cryptography is constrained by high computational complexity and certificate management costs, rendering it impractical for resource-limited IoD nodes. This paper leverages the low computational overhead of Chebyshev polynomials to address the limited computational capability of nodes, proposing a certificateless public key cryptography scheme. Through the semigroup property, it constructs a lightweight authentication and key agreement protocol with identity privacy protection, resolving the security and performance trade-off in dynamic IoD environments. Security analysis and performance tests demonstrate that the proposed scheme resists various attacks while reducing computational overhead by 65% compared to other schemes. This work not only offers a lightweight certificateless cryptographic solution for IoD systems but also advances the engineering application of Chebyshev polynomials in asymmetric cryptography. Full article