Secure Data Transmission Research Articles

Innovative Soft Cryptosystem for Encrypting and Decrypting Messages with IDEA

This paper introduces an advanced cryptosystem for message encryption and decryption, integrating the International Data Encryption Algorithm (IDEA) with soft set and soft matrix principles. Soft sets, conceptualized by Molodtsov, are adept at managing uncertainty, making them ideal for cryptographic frameworks. Our approach utilizes inverse and characteristic products of soft sets and matrices, combined with the robust IDEA algorithm, to significantly enhance security. The IDEA algorithm operates on 64 -bit data blocks with a 128 -bit key, ensuring strong encryption. Additionally, we incorporate the symmetric to introduce complex permutations, further strengthening the cryptographic process by ensuring unique encryptions for identical plaintexts. This layered approach drastically improves resilience against unauthorized decryption. Practical applications demonstrate the system's efficacy, highlighting its superior security and reliability for data transmission in environments with high uncertainty. This innovative cryptosystem provides a formidable solution for contemporary encryption challenges.

Enhancing Smart Healthcare Networks: Integrating Attribute-Based Encryption for Optimization and Anti-Corruption Mechanisms

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 milliseconds, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74%. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

CLOUD DATA PROTECTION USING WEIBULL DISTRIBUTED RECURRENT NEURAL ERGODIC SIGNCRYPTION

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.

Fluid-Infiltrated Metalens-Driven Reconfigurable Intelligent Surfaces for Optical Wireless Communications.

A reconfigurable intelligent surface (RIS), a leading-edge technology, represents a new paradigm for adaptive control of electromagnetic waves between a source and a user. While RIS technology has proven effective in manipulating radio frequency waves using passive elements such as diodes and MEMS, its application in the optical domain is challenging. The main difficulty lies in meeting key performance indicators, with the most critical being accurate and self-adjusting positioning. This work presents an alternative RIS design methodology driven by an all-silicon structure and fluid infiltration, to achieve real-time control of focal length toward a designated user, thereby enabling secure data transmission. To validate the concept, both numerical simulations and experimental investigations of the RIS design methodology are conducted to demonstrate the performance of fluid-infiltrated metalens-driven RIS for this application. When combined with different fluids, the resulting ultra-compact RIS exhibits exceptional varifocal abilities, ranging from 0.4 to 0.5 mm, thereby confirming the adaptive tuning capabilities of the design. This may significantly enhance the modulation of optical waves and promote the development of RIS-based applications in wireless communications and secure data-transmission integrated photonic devices.

К вопросу реализации алгоритмов проектирования защищенных сетей передачи данных

Existing approaches to the design of secure data transmission networks (DRN) are often based on guidelines for the design of communication networks in a broad sense, at the moment there are no clear regulations for the design of communication networks in a secure version from the point of view of information security. In the process of advanced design of the SPDS, problems arise in finding effective solutions for the construction of such communication networks and ensuring the information security of the SPDS in terms of information security tools (SIS) in given areas determined by information security measurements, such as the availability, accessibility, secrecy of information circulating in the SPDS. The article proposes algorithms based on the parameters of non-stationary hyper-network models, which make it possible to implement the analysis and synthesis of ZSPD, as well as take into account the initial data on the intended users, services/applications implemented on ZSPD under conditions of external destructive effects (VDV).

A Network Security Transmission Model Based on Improved Sparrow Search Algorithm

With the development of the socio-economic landscape, the demand for electricity supply is steadily increasing. The application of 5G networks in this field has gradually alleviated the supply-demand response issues associated with large-scale distributed power sources. However, the broadcast nature of 5G channels raises concerns about secure data transmission. Addressing these challenges, this paper establishes a collaborative communication model for large-scale Demand Response (DR). Using the distributed power source network as a foundation, a communication system based on Power Line Communication (PLC) and Generic State Machine (GenSM) is proposed. Building on this, a self-adaptive data transmission algorithm based on the sparrow algorithm is proposed. This algorithm uses swarm intelligence for efficient energy management and secure transmission. Simulation results show that, compared to traditional Multiple-Input Multiple-Output (MIMO) models, under specific conditions, the proposed model achieves an energy efficiency improvement of 131% and 69%.

Integrating IoT, Machine Learning, and Blockchain for Enhanced Network Security and Efficiency: A Review

The rapid expansion of the Internet of Things (IoT) has led to significant improvements in automation, communication, and data exchange across various industries. However, the integration of numerous interconnected devices has also heightened concerns regarding network security, data privacy, and system efficiency. This review explores the intersection of IoT, machine learning (ML), and blockchain technology as a holistic approach to enhancing network security and operational efficiency. IoT devices generate vast amounts of data, which, when combined with machine learning algorithms, can improve predictive analytics, anomaly detection, and real-time decision-making. Blockchain technology further strengthens this ecosystem by providing a decentralized and immutable ledger, ensuring secure data transmission and reducing vulnerabilities. The paper discusses recent advancements, key challenges, and future research directions, highlighting the potential of combining IoT, ML, and blockchain to create robust, secure, and efficient networks. Specific use cases, such as in healthcare, supply chain management, and smart cities, are also examined to illustrate practical implementations. This review concludes with insights into the future potential of these technologies and the challenges that remain in their integration.

SCMABC Algorithm-based routing for secure and efficient data transmission in smart grid AMI networks

SCMABC Algorithm-based routing for secure and efficient data transmission in smart grid AMI networks

Secured Data Transmission in Network using Nano Technology

A Nanonetwork is mutually joined or related nanomachines used in communication system and helps to estimate uncomplicated tasks, making them simple to understand which are able to execute a very easy and uncomplicated tasks and is therefore easy to understand, such as computing, data storing, sensing. Nanonetworks consists of range of operation by permitting them to coordinate, share and combine information. Nanonetworks enables new application of Nanotechnology in biomedical field. The foremost concept behind nanotechnology and nanoscience was to control and manipulate atoms and molecules. The term “nanotechnology” was coined by professor Norio Taniguchi over a decade later. A Nanotechnology was in a position to develop small devices small . A Nanonetwork comprises of nanoscale computing devices and sensors that helps in communication at the nanoscale. Applications in networking consists of wireless technology, IOT, wireless and mobile nano devices. Based on the application requirement nanonetworks classifies the nanomachines to various application requirements like nano transmitters, Nano receiver, Nano Robot, Nano controller. Communication security is taken care from unauthorized access. Security composes of physical security, emission security, encryption security, transmission security. Some of the best tools used for secure business communication are perception point, Mail fence, rocket chat.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Obfuscating Ciphertext-Policy Attribute-Based Re-Encryption for Sensor Networks with Cloud Storage

With the rapid growth of wireless sensor networks, secure data transmission, storage, and distribution in such networks has become an urgent demand. To defend against security risks such as data leakage, key compromise, and unauthorized misuse of data simultaneously, we propose a novel obfuscatable ciphertext-policy attribute-based re-encryption scheme with a specially designed obfuscator. The proposed scheme leverages program obfuscation to transform the re-encryption program codes into an unintelligible form and embed the private keys into the obfuscated implementation. Consequently, the proposed scheme protects data confidentiality and keeps the secrecy of the private key while providing fine-grained access control. Formal proofs for the security of the proposed re-encryption scheme and the obfuscator are provided. Extensive experiments have been conducted on representative platforms, including cloud servers, workstations, and embedded devices, to evaluate the computational efficiency and energy consumption of the scheme. Experimental results indicate that the scheme achieves high efficiency on various platforms and economical energy consumption on typical embedded devices with constrained resources.

A review of research on secret sharing

This article explores the importance and challenges of secret sharing technology in the context of rapid advancements in information technology. With increasing attention on data security and privacy protection, secret sharing serves as a key cryptographic technique that ensures secure data transmission, storage, and multi-party computation by dividing secrets into multiple shares. However, existing solutions encounter performance bottlenecks when dealing with large-scale datasets, managing share updates in dynamic environments, and facing quantum computing threats. This article systematically summarizes the basic concepts, theories, classifications, and characteristics of secret sharing while analyzing its applications in financial security, medical privacy, and other fields. Furthermore, it presents recent research progress on dynamic secret sharing and anti-quantum secret sharing. The findings indicate that dynamic secret sharing schemes improve long-term key storage security, while anti-quantum secret sharing provides a solution to counteract quantum computing threats. This study not only deepens understanding of secret sharing technology but also offers insights for future research directions, significantly contributing to enhancing data protection levels.

Securing data transmission channels between smart devices and the cloud using homomorphic encryption for blood pressure monitoring sensors

Securing data transmission channels between smart devices and the cloud using homomorphic encryption for blood pressure monitoring sensors

Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

Adaptive grouped balanced binary tree based multi user secure data transmission on cloud

Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission.

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master-slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

Robust AI Based Bio Inspired Protocol using GANs for Secure and Efficient Data Transmission in IoT to Minimize Data Loss

Objectives: To propose an AI-based protocol to enhance the reliability and security of IoT data transmission in a robust manner. Deep learning with bio-inspired methods is employed to address real-time challenges such as route optimization, data integrity, error recovery, and end-to-end delay in order to minimize data loss and maximize the transmission rate. Methods: Generative Adversarial Networks (GANs) are used to enhance the robustness of the protocol in combination with a bio-inspired Artificial Immune System (AIS) to detect IoT network anomalies and responds to malicious activities by using the adaptive learning capabilities of IS. Hybrid Automatic Repeat Request (HARQ), an error recovery method, is utilized to detect network errors in order to correct and retransmit data to the destination during error-prone network conditions. Queue learning action sets are used to discover the finest path for efficient data transmission. The OMNeT++ simulator is used to assess the performance of the proposed BIP-GANs protocol. The performance results are compared with the prevailing data transmission protocols, such as EAP-IFBA, DNN-CSO, and ALO-DHT. Findings: The suggested BIP-GANs data transmission protocol outperforms with promising results, with an energy consumption rate of 6%, 8 seconds data transmission speed, 6 second less delay rate, 98% robustness (security and confidentiality), 98.5% alive nodes, and 99% network life span, which is higher than the prevailing methods. Novelty: This research study provides a comprehensive solution to secure and efficient data transmission by integrating the AI-based bio-inspired BIP-GANs method to address the security and data transmission challenges of existing methods such as EAP-IFBA, DNN-CSO, and ALO-DHT in terms of energy consumption rate, transmission speed, delay rate, life span of the network, robustness, and number of alive nodes. Keywords: Artificial Intelligence, Generative Adversarial Networks, Error Recovery, Secured Data Transmission, Deep Learning, Bio­Inspired Algorithm

Securing Data Transmission from Adversaries in Cybersecurity WSN Using Efficient Key Management Techniques

Concern over cyber security is becoming more and more prevalent in today's international politics. National security is now evaluated not only in terms of military might or economic might, but also in terms of technological innovation. The fact that today's society is heavily dependent on computer communication and control systems, which opens up infinite opportunities, shows how important technology is in the national security area. By effectively changing the keys, the suggested work aimed to provide WSN with complicated security solutions that would ensure data integrity, security, and genuine transmission for many additional network operations through structured cryptography approaches. The study's protective measures can be applied to a variety of WSN configurations, including heterogeneous WSNs with and without mobility aid and static homogeneous WSNs. Because WSN is more limited to hardware resources, not all applications can be protected using the same method.

A Secure Fusion: Elliptic Curve Encryption Integrated with LSB Steganography for Hidden Communication

In today's digital age, ensuring secure communication is essential. This article presents a novel approach for hidden communication by integrating Elliptic Curve Encryption (ECE) with Least Significant Bit (LSB) Steganography. Our proposed fusion offers a robust solution, Stegno Curve for concealing sensitive information within innocuous cover media while encrypting it using elliptic curve cryptography. By leveraging the strengths of both techniques, we achieve enhanced security and confidentiality in data transmission. Through a comprehensive literature review, methodology explanation, security analysis, and implementation details, we demonstrate the feasibility and effectiveness of the Stegno Curve Method. The findings of this study not only contribute to advancing the field of secure communication but also open avenues for practical applications in various domains, such as secure cloud transitions, smart home technologies, and data encryption.

Energy Efficient Approach with ANN based Intrusion Detection System for Securing Network and Route for Transmission in Wireless Sensor Network

The research focuses on hierarchical clustering-based intrusion detection using artificial neural networks (ANNs) for secure data transmission in wireless sensor networks (WSNs). WSNs consist of numerous tiny sensor nodes deployed to monitor environmental phenomena. These nodes face significant challenges, including restricted energy, memory space, communication range, and limited capacity for managing energy, storing, transmitting, and processing data. To address these limitations, a machine learning-based approach is proposed to detect intrusions and efficiently utilize energy by properly selecting cluster heads using a secure clustering protocol. The proposed method was implemented and tested using MATLAB software, employing the NSLKDD and UNSW-NB15 datasets for intrusion detection. The results demonstrated promising outcomes in detecting intruders and enhancing network efficiency, achieving a 92% packet delivery ratio (PDR) and 1.82 Mbps throughput. The study concludes that while WSNs are gaining popularity due to their simplicity, flexibility, and scalability, innovative solutions are necessary for efficient energy management and security. Future research should focus on advanced machine learning models, energy harvesting techniques, scalable protocols, real-time data processing, and integration with IoT platforms for broader applications and enhanced functionality.

Blockchain-Based LSTM Approach for Secure Data Transmission in IoT-Enabled Healthcare System

Blockchain technology can help with a number of issues facing the healthcare sector. Its potential as trust brokers includes the provision of incentive machines and the facilitation of innovative healthcare solutions. Additionally, its potential as trust brokers includes the facilitation of novel business models that offer fresh perspectives on the interactions between patients and providers, among other diverse healthcare stakeholders. Blockchain, for instance, makes incentive structures easier to implement and promotes decentralised trust in patient-centered healthcare models and global health information exchange (HIE). On the other hand, blockchain is characterised as a decentralised network or service that can help remove obstacles in the healthcare sector. The healthcare sector has benefited greatly from the effective resolution of problems relating to adoption and technological obstacles, interoperability and integration with current systems, cost unpredictability, scale, and regulatory compliance through the use of Blockchain technology. This work proposed discusses fault tolerance techniques in relation to sharing health-related data.