The rapid growth of vehicular social networks (VSNs) within the social internet of vehicles (SIoV) ecosystem has introduced critical demands for secure, privacy-preserving, and quantum-resilient data exchange mechanisms. Existing authentication protocols often rely on traditional cryptographic primitives such as elliptic curve cryptography (ECC) and bilinear pairings, both of which rely on the hardness of discrete logarithm and pairing problems. These assumptions are efficiently solvable using Shor's quantum algorithm, rendering ECC and bilinear schemes insecure in the presence of quantum adversaries. To address these limitations, we propose a novel enhanced effective authentication approach for data exchange (EAADE), a lattice-based authentication protocol that integrates ephemeral pseudonymization, spatial cloaking, and federated learning to enable secure model sharing among vehicles without exposing sensitive data. The protocol provides mutual authentication among vehicles, roadside units (RSUs), and the main server while ensuring forward secrecy, post-quantum security, and strong anonymity. Security is validated using both formal automated validation of internet security protocols and applications (AVISPA) and informal analysis, confirming resistance to Sybil, replay, man-in-the-middle (MITM), and de-anonymization attacks. Extensive simulations using OMNeT++, SUMO, and federated learning frameworks show that enhanced EAADE reduces computation cost by 44.96%, communication overhead by 22.16%, authentication delay by 17.65%, and packet loss by 23.64%, compared to existing schemes. These results demonstrate the protocol's efficiency, scalability, and readiness for next-generation vehicular networks.

The Internet of Things (IoT) introduces new security considerations because connected devices typically have limited computational resources, making traditional cryptographic algorithms inefficient. RSA, a widely used public-key cryptosystem, is particularly resource-intensive due to its large key sizes and heavy arithmetic operations. This study investigates the inefficiency of standard RSA in resource-constrained IoT environments and evaluates optimization strategies aimed at improving performance without compromising security. Enhancements include key size reduction, the use of the Chinese Remainder Theorem (CRT) for decryption, and precomputation techniques. Performance was assessed in a simulated IoT environment, measuring execution time, memory consumption, and energy efficiency. Security considerations, including potential vulnerabilities of CRT-based RSA such as differential fault attacks, were addressed through redundancy and verification mechanisms. Comparative insights with Elliptic Curve Cryptography (ECC) and post-quantum lightweight schemes (e.g., Ascon, Kyber-SLH-DSA) were provided to contextualize the results. The findings show that optimized RSA achieves significant reductions in computational overhead and energy consumption while maintaining correctness, demonstrating its feasibility for low-power IoT devices. Hybrid cryptography approaches, combining RSA key exchange with symmetric AES payload encryption, are recommended for future implementations. These results reinforce that efficient and practical public-key encryption is achievable in constrained IoT systems while preserving strong security.

The integration of the Internet of Things (IoT) and edge computing is transforming healthcare by enabling real-time acquisition, processing, and exchange of sensitive patient data close to the data source. However, the distributed nature of IoT-enabled smart healthcare systems exposes them to severe security and privacy risks during health information exchange (HIE). This study proposes an edge-enabled hybrid encryption framework that combines elliptic curve cryptography (ECC), HMAC-SHA256, and the Advanced Encryption Standard (AES) to ensure data confidentiality, integrity, and efficient computation in healthcare communication networks. The proposed model minimizes latency and reduces cloud dependency by executing encryption and verification at the network edge. It provides the first systematic comparison of hybrid encryption configurations for edge-based HIE, evaluating CPU usage, memory consumption, and scalability across varying data volumes. Experimental results demonstrate that the ECC + HMAC-SHA256 + AES configuration achieves high encryption efficiency and strong resistance to attacks while maintaining lightweight processing suitable for edge devices. This approach provides a scalable and secure solution for protecting sensitive health data in next-generation IoT-enabled smart healthcare systems.

The Internet of Things (IoT) has improved efficiency and quality of life by connecting devices to the internet. It has seen success in areas such as smart vehicles and Unmanned Aerial Vehicles (UAVs), but faces processing limitations due to the need to send large amounts of data to other devices for processing. When heavy processing is required, it uses offloading techniques to send the data to other devices for processing. Secure data offloading transmission remains a fundamental challenge in this field. This paper presents an innovative authentication and key exchange method that uses Elliptic Curve Cryptography (ECC) and incorporates Handover for secure offloading, offering a safe, lightweight solution within a blockchain network. To evaluate the resistance of the proposed scheme against active and passive attacks, we employed the AVISPA tool to apply both formal and informal methods. Subsequently, to demonstrate the scheme's lightweight nature, we examined it with respect to computation and communication costs, the number of bits used, and security requirements. Additionally, we simulated the proposed scheme using the NS3 tool in two scenarios: urban and highway, with varying numbers of vehicles. The results indicate that the proposed scheme performs acceptably in urban and highway scenarios.

As technology continues to evolve, cybersecurity measures tend to be vulnerable to the computational power of quantum computers. These computers perform calculations faster than classical computers. This ability to solve tasks within polynomial time threatens current cybersecurity practices through Shor's and Grover's algorithms. Classical computers rely on mathematical hardness assumptions and are vulnerable to quantum attacks. This paper scrutinizes the double effects of quantum computing on cybersecurity and its ability to support post-quantum resistant technologies. A systematic literature review (SLR) of 24 peer-reviewed articles (2021-2025) obtained from IEEE Xplore, SpringerLink, ACM, and Google Scholar was conducted, and the results identified three integral themes. Firstly, 80% of quantum computing threats studies analysed prove that Shor's algorithm can efficiently factorise large integers, rendering Rivest Shamir Alderman and Elliptic Curve Cryptography obsolete. Secondly, 65% of the studies show that Post-Quantum Cryptography (PQC) offers quantum-resilience in the foreseeable future. In comparison, 25% of Quantum Key Distribution (QKD) papers show practical barriers like signal loss and standardization delays. 15% of studies reveal the urgent need for regulatory and ethical concerns. Key results highlight the urgent need for hybrid cryptographic systems that combine quantum key distribution and post-quantum cryptography, as proposed by 40% of recent publications. 46% of studies show that Europe leads quantum cybersecurity research, driven by collaborative policy efforts. This study suggests practical recommendations for accelerated adoption of NIST-standardised PQC algorithms, investment in QKD infrastructure for critical sectors, and multidisciplinary collaboration to address technical, legal, and ethical gaps. This paper provides a roadmap for mitigating quantum threats and leveraging quantum technologies to transform cybersecurity resilience in the digital era.

Current healthcare data systems face major challenges in preventing unauthorized access, ensuring compliance with data privacy regulations, and enabling intelligent secondary use of patient information. To address these issues, we introduce cluster-based analysis with machine learning for enhanced healthcare data security (CAML-EHDS), a unified framework that combines homomorphic encryption, attribute-based elliptic curve cryptography (ECC), and semantic clustering with machine learning. CAML-EHDS improves upon existing models by offering fine-grained access control, adaptive threat detection, and data-driven insights while preserving privacy. Experimental results show that CAML-EHDS achieves up to 98% classification accuracy with low node count, and maintains 94% accuracy even at high node distribution levels, while ensuring encryption time under 24 seconds and acceptable data loss below 29%. Moreover, in comparative analysis with state-of-the-art models (support vector machine (SVM), random forest (RF), and decision tree (DT)), CAML-EHDS outperforms all in key metrics with an accuracy of 0.96. These results demonstrate CAML-EHDS’s potential for real-world deployment in secure, scalable, and intelligent healthcare environments, including privacy-aware digital marketing integration.

The dark web is a hidden part of the internet that allows users to communicate securely and anonymously, often using applications such as Tor. This paper specifically addresses the use of Elliptic Curve Cryptography (ECC) for enhanced security within a dark web context, where, although traditional cryptographic algorithms, such as RSA, possess unassailable cryptographic value, they are often computationally inefficient for non-standard computing environments, and do not scale well. We compare ECC and RSA performance in terms of key generation time, encryption/decryption time, and memory usage, and find that ECC outperforms RSA across all metrics in challenging, limitedresource networks. In our testing, we simulate the real-world operational environment of anonymizing networks by using test messages and message flow logs that are anonymized. We demonstrate the relative improvements in computational time and memory usage of ECC over RSA while maintaining equivalent cryptographic strength. Using these results, we create an integrated multi-layered security construct, which uses ECC, evaluates and classifies threat information using machine learning methods to detect anomalies in near real-time, and constructs a blockchain model to allow decentralized audit trail tracking, resulting in a substantially enhanced security and privacy solution to address the unique requirements of anonymous communication in a dark web environment. This study helps to address the lack of empirical evaluations of ECC in dark web contexts, presenting a practical roadmap for implementing innovative cryptographic and analysis protocols for digital anonymity. Various outcomes support the efficacy of pairing lightweight encryption with intelligent behavioural analytics to counter evolving cyber threats. The framework provides a scalable, flexible, and consistently relevant option for countering a rapidly changing threat while enabling future work on post-quantum cryptography.

The rapid growth of Cloud Infrastructure (CI) has enabled advancements across domains such as healthcare, finance, and industrial automation. However, sensitive data in CI remains is vulnerable to various threats such as replay attacks, man-in-the-middle attacks, impersonation, and credential theft, especially over public networks. To address these challenges in this paper, a Secure Multi-Phase Authentication Protocol for Cloud Infrastructure (SMPA-CI) based on Elliptic Curve Cryptography has been proposed. The proposed protocol incorporates four phases such as system initialization, user registration, login authentication, and secure password update, where each employs lightweight cryptographic techniques, including digital signatures, elliptic curve-based key exchange, and hash functions. Unlike conventional authentication schemes, SMPA-CI emphasizes mutual authentication, user anonymity, and resistance to prevalent attack vectors, while maintaining efficiency suitable for resource-constrained devices. Experimental results demonstrate that SMPA-CI reduces computational cost by up to 18%, lowers communication overhead by 15%, and decreases authentication latency compared to existing ECC-based schemes. These results highlight the novelty and practicality of SMPA-CI in achieving a secure, scalable, and performance-aware authentication framework for modern cloud infrastructures.

The explosive growth in Internet of Things (IoT) technologies has given rise to significant security concerns, especially with the emergence of sophisticated and zero-day malware attacks. Conventional malware detection methods based on static or dynamic analysis often fail to meet the real-time operational needs and limited-resource constraints typical of IoT systems. This paper proposes TRIM-SEC (Transformer-Integrated Malware Security and Encryption for IoT), a lightweight and scalable framework that unifies intelligent threat detection with secure data transmission. The framework begins with Autoencoder-Based Feature Denoising (AEFD) to eliminate noise and enhance input quality, followed by Principal Component Analysis (PCA) for efficient dimensionality reduction. Malware classification is performed using a Transformer-Augmented Neural Network (TANN), which leverages multi-head self-attention to capture both contextual and temporal dependencies, enabling accurate detection of diverse threats such as Zero-Day, botnets, and zero-day exploits. For secure communication, TRIM-SEC incorporates Lightweight Elliptic Curve Cryptography (LECC), enhanced with Particle Swarm Optimization (PSO) to generate cryptographic keys with minimal computational burden. The framework is rigorously evaluated against advanced baselines, including LSTM-based IDS, CNN-GRU hybrids, and blockchain-enhanced security models. Experimental results show that TRIM-SEC delivers higher detection accuracy, fewer false alarms, and reduced encryption latency, which makes it well-suited for real-time operation in smart IoT ecosystems. Its balanced integration of detection performance, cryptographic strength, and computational efficiency positions TRIM-SEC as a promising solution for securing next-generation IoT environments.

Security enhancement of three POMs based interference algorithm using elliptic curve cryptography

The rapid adoption of the Internet of Things (IoT) in healthcare has revolutionized patient monitoring and real-time medical decision-making but also introduced significant security and privacy challenges. To address these issues, this paper proposes SecHealth, a lightweight trusted framework for secure data exchange and proactive threat mitigation in IoT-enabled healthcare systems. The framework integrates three core components: a multi-layered trust management mechanism, an advanced lightweight ECC-based encryption protocol (LECCEP-A), and a robust hybrid anomaly detection system (RHADS). Trust is computed using behavioral, communication, and contextual parameters, dynamically updated using feedback-based learning and anomaly filtering. LECCEP-A provides low-latency, secure data transfer from external attacks and entropy-augmented encryption based on elliptic curve cryptography. RHADS combines machine learning techniques (LSTM, VAE, SVM) and probabilistic reasoning to detect sophisticated attacks. The proposed system was evaluated in a MATLAB-based simulated healthcare IoT network consisting of 100–500 heterogeneous devices under mixed attack scenarios. The performance of suggested framework was measured using critical metrics such as latency, energy efficiency, throughput, detection accuracy, and false positive rate (FPR). It achieved anomaly detection accuracy of 98.1%, FPR of only 2.1%, latency of 85–95 ms, energy efficiency of 0.68–0.78 J/node, and throughput of 155–180 Kbps, outperforming two recent benchmark models1,2 by 4–7% in accuracy and 20–40% in efficiency. The recommended framework effectively mitigates both internal and external malicious behaviours and threats while preserving data confidentiality, integrity, and trust. Its flexible and scalable architecture makes it deployable in real-world healthcare infrastructures with constrained devices.

The integration of federated learning into Industrial Internet of Things (IIoT) networks enables collaborative intelligence but also exposes systems to identity spoofing, model poisoning, and malicious update injection. This paper presents Leash-FL, a lightweight self-healing framework that combines certificateless elliptic curve cryptography with blockchain to enhance resilience in resource-constrained IoT environments. Certificateless ECC with pseudonym rotation enables efficient millisecond-scale authentication with minimal metadata, supporting secure and unlinkable participation. A similarity-governed screening mechanism filters poisoned and free-rider updates, while blockchain-backed checkpoint rollback ensures rapid recovery without service interruption. Experiments on intrusion detection, anomaly detection, and vision datasets show that Leash-FL sustains over 85 percent accuracy with 50 percent malicious clients, reduces backdoor success rates to under 5 percent within four recovery rounds, and restores accuracy up to three times faster than anomaly-screening baselines. The blockchain layer achieves low-latency consensus, high throughput, and modest ledger growth, significantly outperforming Ethereum-based systems. Membership changes are efficiently managed with sub-50 ms join and leave operations and re-admission within 60 ms, while guaranteeing forward and backward secrecy. Leash-FL delivers a cryptography-driven approach that unifies lightweight authentication, blockchain auditability, and self-healing recovery into a secure, resilient, and scalable federated learning solution for next-generation IIoT networks.

This research adopts a hybrid multi-factor authentication (MFA) model that merges Elliptic Curve Cryptography (ECC) with symmetric encryption through AES algorithm implementation in place of Twofish. The model adopts simple, effective cryptographic procedures designed for limited-resource edge machines. The evaluation covers 50 simulation rounds to analyze encryption-decryption speed and data size together with generation time. The results show that ECC encryption together with AES encryption completes operations in less than one microsecond, which verifies their functionality for secure authentication systems in real time. The simulated MFA employs password, OT, P, and ECC-based keys to establish multi-layered security within distributed edge computing platforms.

ABSTRACT Wireless body sensor networks (WBSNs) are increasingly used in healthcare for remote monitoring of patients. Although these systems improve access to medical care, they also face serious challenges related to data security and patient authentication. This study proposes a lightweight and secure authentication framework based on a Three‐Tier Secure Message Authentication Code (TTSMAC) protocol. The framework combines three key techniques: Factorized RSA (FRSA) for efficient key generation, Length Pearson Hashing (LPH) for secure token management, and Dual Secret Key Elliptic Curve Cryptography (DSK‐ECC) for protecting stored data. Experimental results showed that the proposed framework reduces encryption/decryption time, lowers key setup overhead, and achieves higher throughput compared with existing methods. Also, the performance evaluations showed substantial improvements in encryption/decryption times and throughput, demonstrating the framework's suitability for resource‐constrained, battery‐powered wearable sensors. Overall, the framework enhances security, maintains patient data privacy, and ensures reliable authentication for WBSN‐based healthcare applications.

ABSTRACT The proliferation of remote patient monitoring presents new possibilities for the enhancement of smart healthcare systems. Wireless body area networks ( WBANs ) are an emerging technology for building a successful healthcare system. WBANs are integrated with the cloud to provide successful remote monitoring. However, WBANs are exposed to a variety of cryptographic attacks due to limited computational power. Several authentication approaches have been presented in recent years to address the security issues in WBANs . Yet, most authentication schemes failed to provide secure authentication and key agreement due to their reliance on computationally intensive operations. Therefore, we propose a secure authenticated key agreement approach for WBANs , emphasizing minimal computational overhead by utilizing lightweight cryptographic primitives, such as elliptic curve cryptography ( ECC ) and hash functions . The three‐way authentication process is used in this study to offer a safe mutual authentication approach between the patient and the healthcare service provider. Automated validation of internet security protocols and applications ( AVISPA ) has been used to verify the security of the proposed scheme in the presence of the Dolev‐Yao ( DY ) adversary model. Moreover, the security of the proposed scheme is also illustrated using Burrows‐Abadi‐Needham ( BAN ) predicates and the Real‐or‐Random ( ROR ) model. The performance evaluation in terms of computational complexity, along with the comparative analysis, demonstrates that the proposed approach provides enhanced security compared to existing approaches.

Non-fungible tokens (NFT) have recently become a popular method of tokenizing \& commercializing personal artifacts. Designing NFTs requires selecting different blockchain-based consensus models, encryption techniques, and distribution mechanisms. Existing NFT design techniques use computationally complex encryption models like Elliptic Curve Cryptography (ECC), Advanced Encryption Standard (AES), etc., which restricts their general-purpose usability, limiting their scalability for real-time use cases. To overcome this drawback, while maintaining high security, this text proposes a design of a lightweight, restrictive non-fungible token based on Practically Unclonable Functions (PuFs) via image signature patterns. The proposed model initially collects context-specific information sets about the entity that needs tokenization and uses this information to generate restrictive hash sets. These hash sets are passed through a customized PuF model, which generates image-like hash signatures. The generated hash signatures are iteratively embedded into unique images, which are fused via a dual visual encryption-decryption process. The encryption process generates 2 image sets, for distribution among the buyer \& seller, while the decryption process aggregates these image sets to form a single file token. These tokens are passed through another encryption-decryption-based validation process while reselling operations. Due to use of PuFs and restrictive hash sets, the proposed model is capable of deployment for low-power IoT applications and can be scaled for general-purpose scenarios. The proposed model was tested on different NFT use cases, and showcased 10.4% lower processing delay, 8.3% lower energy consumption during selling, and 4.9% lower energy consumption during reselling processes. The tokens generated via this model were also tested under different attack types, and similar efficiency levels were observed under real-time scenarios.

The vision of sustainable development goal 9 (SDG 9) is realized through the integration of innovative technologies in the cyber-physical system (CPS). This work focuses on a smart network meter (SNM) application, designed to manage the extensive big data analytics required for processing and analyzing vast amounts of aggregated data in a short period. To address these demands, an advanced explicitly parallel instruction computing (AEPIC) approach is employed, leveraging a multi-core hardware security module (HSM) built on the elliptic curve cryptography (ECC) algorithm. Implementing the algorithm on various field programmable gate arrays (FPGAs) ensures adaptability to different hardware configurations, delivering scalable and optimized performance for big data aggregation in SNM applications. The proposed module showcases exceptional performance in design analysis. The Virtex-7 FPGA demonstrates excellent suitability for big data analytics in smart network applications, with dynamic power consumption accounting for 55% of total power and an on-chip power of 0.542 watts.

Not only is quantum computing a game changer but it is also an unprecedented threat to world security in cyberspace. Although quantum systems are expected to transform scientific computing, they are also endangering the very notion of classical cryptography, specifically, the public key system of RSA and elliptic curve cryptography (ECC). The problems in computation that such schemes are built on, including integer factorization and discrete logarithms, can be efficiently solved using quantum algorithms, including Shor. This makes the secured communication network by classical encryption susceptible to quantum attacks in the future. In turn, current quantum-resistant or post-quantum cryptographic algorithms (PQC) are resistant to classical and quantum adversaries. The article provides a general overview of significant families of PQC, such as lattice-based, code-based, multivariate polynomial, hash-based and isogeny-based, and discusses their relevance to the provisioning of communication networks. The work illuminates to some degree the advances toward standardization already being undertaken by the National Institute of Standards and Technology (NIST) by analysing its security underpinnings, trade-offs in its operation and its readiness to deploy. Experiments show that lattice based (CRYSTALS-Kyber, Dilithium) and code based (McEliece) are both well-theoretically secure as well as high performance, and that systems based on code (McEliece) have a long track record of reliability at the cost of large key-sizes. Other issues which have been talked about in the paper are interoperability issues, migration policies and side-channel attack defence. It finds conclusively that quantum-resistant cryptography is the next step that must be regarded as the most important to secure the secrecy and integrity of communication networks in the future.

This paper introduces MedFusionNet, a hybrid privacy-preserving scheme that combines Federated Learning (FL), Blockchain and BiLSTM-Autoencoders to perform secure analysis of Electronic Health Record (EHR). In this approach, the model is jointly trained across institutions without exchanging raw data (by using Federated Averaging, or FedAvg), in line with the privacy regulations. Security enforced usage of Elliptic Curve Cryptography (ECC) to achieve efficient encrypted transmission and Attribute-Based Encryption (ABE) in achieving fine grained role based access control. When tested on the MIMIC-IV database under simulated federated settings, MedFusionNet attained an Area Under the ROC Curve (AUROC) of 0.96, an F1-score of 0.95, and an accuracy of 95.1% in comparison to baseline methods. These findings show its potential to achieve good predictive performance as well as protect the privacy of the patient. The presence of scalability, security, and accuracy emplaces MedFusionNet as an achievable answer to healthcare analytics and it can be expanded in the future to realistic distributed systems, client heterogeneity, and complex aggregation policies to further increase resilience and performance.

The integration of Industrial Automation Systems (IAS) with the Internet of Things (IoT) under Industry 4.0 has significantly enhanced operational efficiency but also exposed critical communication infrastructures to cyber threats. Conventional security frameworks often fail to ensure end-to-end data integrity, authentication, and confidentiality in real-time industrial networks. This paper proposes a blockchain-enabled mathematical cryptography model designed to secure data transmission between industrial nodes. The framework utilizes Elliptic Curve Cryptography (ECC) for lightweight key generation, SHA-3 hashing for immutable transaction records, and smart contract-based consensus for autonomous trust management within a distributed ledger. A simulated industrial environment demonstrates that the proposed model achieves 42% faster encryption-decryption cycles and a 38% reduction in data latency compared to traditional asymmetric cryptosystems. The mathematical foundation ensures provable security under discrete logarithm assumptions, while blockchain consensus guarantees tamper resistance and auditability. This study contributes a scalable, mathematically robust architecture for secure data transmission in automation networks, offering potential integration within Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) environments.