Secure Data Transfer Research Articles

Proposal of an optimum method of audio steganography to secure data transfer

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

Advanced Encryption Techniques for Securing Data Transfer in Cloud Computing: A Comparative Analysis of Classical and Quantum-Resistant Methods

Advanced Encryption Techniques for Securing Data Transfer in Cloud Computing: A Comparative Analysis of Classical and Quantum-Resistant Methods

Hyper Chaotic Chen Model-Based Medical Image Encryption and DNA Coding Framework for Secure Data Transfer Critical Infrastructures

Hyper Chaotic Chen Model-Based Medical Image Encryption and DNA Coding Framework for Secure Data Transfer Critical Infrastructures

A secure and efficient blockchain enabled federated Q-learning model for vehicular Ad-hoc networks

Vehicular Ad-hoc Networks (VANETs) are growing into more desirable targets for malicious individuals due to the quick rise in the number of automated vehicles around the roadside. Secure data transfer is necessary for VANETs to preserve the integrity of the entire network. Federated learning (FL) is often suggested as a safe technique for exchanging data among VANETs, however, its capacity to protect private information is constrained. This research proposes an extra level of security to Federated Q-learning by merging Blockchain technology with VANETs. Initially, traffic data is encrypted utilizing the Extended Elliptic Curve Cryptography (EX-ECC) technique to enhance the security of data. Then, the Federated Q-learning model trains the data and ensures higher privacy protection. Moreover, interplanetary file system (IPFS) technology allows Blockchain storage to improve the security of VANETs information. Additionally, the validation process of the proposed Blockchain framework is performed by utilizing a Delegated Practical Byzantine Fault Tolerance (DPBFT) based consensus algorithm. The proposed approach to federated Q-learning offered by Blockchain technology has the potential to develop VANET safety and performance. Comprehensive simulation tests are performed with several assessment criteria considered for number of vehicles 100, Throughput (102465.8 KB/s), Communication overhead (360.57 Mb), Average Latency (864.425 ms), Communication Time (19.51 s), Encryption time (0.98 ms), Decryption time (1.97 ms), Consensus delay (50 ms) and Validation delay (1.68 ms), respectively. As a result, the proposed approach performs significantly better than the existing approaches.

A Scalable Framework for Secure and Reliable Wireless-based Fog Cloud Communication

—Wireless telecommunication systems are essential in transferring data through fog cloud servers. However, the fog cloud servers suffer from unreliable and non-secure routing and limited power resources when mobile users are mobile. This paper introduces a scalable framework (SFRRDC) for reliable wireless routing and secure data transfer. We aim to address security and reliability issues by combining pheromone termite (PT) characteristics and ant colony optimization (ACO) algorithms for high-performance, more secure, highly reliable data transfer among the fog cloud servers. For that, the proposed SFRRDC supports a user authentication mining (UAM) algorithm to secure the confidentiality of the users. Based on testing results, it is confirmed that the proposed SFRRDC provides a better solution for confidentiality and fast routing for a wireless telecommunication system. The results show that the proposed SFRRDC framework’s energy consumption outperforms competing frameworks’ energy consumption with a better-achieved latency. For example, the suggested SFRRDC method uses 748.5 Joules during 72 hours of operation, competing frameworks use more energy, and DDFQFR uses 976.22 Joules. It also shows that the proposed SFRRDC is immune to malicious attacks. The results show that with 3,600 Fog cloud users, the proposed HEE protocol reduces the number of attacks to only 48 compared to 58, 72, and 77415 expected malicious attacks when using the ICDRP-F-SDVN, ACO, and DDFQFR frameworks, respectively.

A Study on Encryption in Poly Alphabetic Ciphers

Internet in nowadays has been in use in all departments of a society and it has been used by people of all ages for different purposes. With its increasing usage, security has become a major concern for secure data transfer over the internet. At this juncture, the importance of cryptography peeks in. Poly alphabetic cipher is one among the types of cryptography in which an alphabet can be substituted to any other alphabet. This article focuses light on different methods of generating Cipher text on the basis of Poly Alphabetic Cipher.

An Explainable Deep Learning-Enhanced IoMT Model for Effective Monitoring and Reduction of Maternal Mortality Risks

Maternal mortality (MM) is considered one of the major worldwide concerns. Despite the advances of artificial intelligence (AI) in healthcare, the lack of transparency in AI models leads to reluctance to adopt them. Employing explainable artificial intelligence (XAI) thus helps improve the transparency and effectiveness of AI-driven healthcare solutions. Accordingly, this article proposes a complete framework integrating an Internet of Medical Things (IoMT) architecture with an XAI-based deep learning model. The IoMT system continuously monitors pregnant women’s vital signs, while the XAI model analyzes the collected data to identify risk factors and generate actionable insights. Additionally, an efficient IoMT transmission model is developed to ensure reliable data transfer with the best-required system quality of service (QoS). Further analytics are performed on the data collected from different regions in a country to address high-risk cities. The experiments demonstrate the effectiveness of the proposed framework by achieving an accuracy of 80% for patients and 92.6% for regional risk prediction and providing interpretable explanations. The XAI-generated insights empower healthcare providers to make informed decisions and implement timely interventions. Furthermore, the IoMT transmission model ensures efficient and secure data transfer.

Self-Sovereign Management Scheme of Personal Health Record with Personal Data Store and Decentralized Identifier

Conventional personal health record (PHR) management systems are centralized, making them vulnerable to privacy breaches and single points of failure. Despite progress in standardizing healthcare data with the FHIR format, hospitals often lack efficient platforms for transferring PHRs, leading to redundant tests and delayed treatments. To address these challenges, we propose a decentralized PHR management system leveraging Personal Data Stores (PDS) and Decentralized Identifiers (DIDs) in line with the Web 3.0 model. Our system features secure interoperability and personal identification masking. Interoperability is achieved through DID digital certificates for verifying PDS addresses and a dynamic access key (AK) system to minimize credential exposure. Data de-identification, including anonymization and encryption, ensures privacy and prevents unauthorized access. We developed a prototype using the Solid open-source library and Hyperledger Aries protocol. Testing showed efficient performance, with DID validations and AK generation under one second, and data operations for 500 MB-sized PHRs completing in two seconds. De-identification processes were both effective and timely. The system demonstrated the ability to manage PHRs securely, empower users with control over their healthcare data, facilitate seamless and secure data transfer between patients and medical entities, and prevent exposure of sensitive information. This approach advances decentralized PHR management, supporting improved healthcare outcomes and patient experiences in the digital era.

Convergence of SaaS, AI, and Telecom in Telehealth: Transforming the future of healthcare delivery through intelligent systems

This paper aims at identifying and analyzing how SaaS, AI, and Telecom are changing the way healthcare is delivered by focusing on telehealth. The goal is to determine the current status of these combining technologies, identify future developments and trends in intelligent telehealth systems, and analyze the opportunities and risks associated with their implementation. Following a systematic review, relevant publications published between January 2014 and January 2024 were gathered from various databases. Finally, eight studies were included for analysis, after exclusion and inclusion criteria were applied. The findings show that SaaS, AI, and Telecom are transforming telehealth by improving the remote patient monitoring, the access to care, and the moving from the reactive to the proactive approach to health. SaaS enhances adaptable and affordable approaches, while AI infuses precise identification and reasoning. Telecom provides secure and steady communication and data transfer while facing issues regarding data security, legislation acts, and disparities in infrastructure. Overall, these research studies highlight the key possibilities of developing the application of telehealth through the integration process of these technologies.

Display Field Communication: Enabling Seamless Data Exchange in Screen–Camera Environments

Display field communication (DFC) is an emerging technology that enables seamless communication between electronic displays and cameras. It utilizes the frequency-domain characteristics of image frames to embed and transmit data, which are then decoded and interpreted by a camera. DFC offers a novel solution for screen-to-camera data communication, leveraging existing displays and camera infrastructures. This makes it a cost-effective and easily deployable solution. DFC can be applied in various fields, including secure data transfer, mobile payments, and interactive advertising, where data can be exchanged by simply pointing a camera at a screen. This article provides a comprehensive survey of DFC, highlighting significant milestones achieved in recent years and discussing future challenges in establishing a fully functional DFC system. We begin by introducing the broader topic of screen–camera communication (SCC), classifying it into visible and hidden SCC. DFC, a type of spectral-domain hidden SCC, is then explored in detail. Various DFC variants are introduced, with a focus on the physical layer. Finally, we present promising experimental results from our lab and outline further research directions and challenges.

Advancing IoT security: A novel intrusion detection system for evolving threats in industry 4.0 using optimized convolutional sparse Ficks law graph point trans-Net

With the rapid advancement of Industry 4.0, the integration of Internet of Things (IoT) strategies in industrial environments has increased exponentially. While this integration enhances productivity and efficiency, it also introduces significant security vulnerabilities. Previous research has employed several deep learning approaches for intrusion detection; however, these methods often suffer from insufficient accuracy, increased computational time, complexity, and higher error rates. To address these issues, this work proposes an innovative solution: "Advancing IoT Security: A Novel Intrusion Detection System (IDS) for Evolving Threats in Industry 4.0 using optimized Convolutional Sparse Fick's Law Graph Pointtrans-Net (CSFLGPtrans-Net)." The proposed system utilizes a comprehensive intrusion detection dataset composed of four different datasets: ToN-IoT, NSL-KDD, CSE‑CIC‑IDS2018, and IoT_bot. Initially, the input data undergoes a pre-processing stage that includes cleaning columns and rows, encoding features, and normalizing data. Following this, a hybrid optimization method, combining the Fire Hawk Optimizer with the Spider Wasp Optimizer, is applied for feature selection. This step is crucial for identifying the most significant features to enhance classification accuracy. The refined data is then classified using the CSFLGPtrans-Net model. To ensure secure data transfer, Fuzzy-based Elliptic Curve Cryptography (FECC) is employed. Experimental simulations conducted on the Python platform demonstrate that the proposed method outperforms existing approaches across various performance metrics, achieving a higher accuracy of 98% and a recall of 0.993. These results highlight the method's superior efficiency and potential for further advancement in securing Industry 4.0 environments.

Cryptography in Network Security: a Mini Review

Network security and cryptography are crucial components in safeguarding data and communication in the digital age. Cryptography plays a vital role in network security by converting plain text into unreadable formats to protect confidentiality and integrity. As organizations generate vast amounts of data daily, ensuring secure data transfer over the internet becomes increasingly important, with network security challenges growing as society transitions to the digital era. Techniques such as encryption are used to protect enterprise information and communication from cyber threats, ensuring the integrity, confidentiality, and accessibility of computer networks and data. Encryption and network security are employed to protect data stored in systems and exchanged over wireless networks, with cryptography utilizing various algorithms and approaches to secure networks and data effectively.

Security of End-to-End medical images encryption system using trained deep learning encryption and decryption network

The Internet of Medical Things (IoMT) links medical devices and wearable, enhancing healthcare. To secure sensitive patient data over the IoMT, encryption is vital to retain confidentiality, prevent tampering, ensure authenticity, and secure data transfer. The intricate neural network architecture of deep learning models adds a layer of complexity and non-linearity to the encryption process, rendering it highly resistant to plaintext attacks. Specifically, the Cycle_GAN network is used as the leading learning network. This work suggests deep learning-based encryption for medical images using Cycle_GAN, a Generative Adversarial Network. Cycle_GAN changes images without paired training data that improves quality and feature preservation. Unlike conventional image-to-image translation techniques, Cycle_GAN doesn’t require a dataset with corresponding input–output pairs. Traditional methods typically needs paired data to learn the mapping between input and output images. Paired data can be challenging to obtain, specifically in medical imaging where gathering and annotating data can be time-consuming, laborious and expensive. The use of Cycle_GAN overwhelms this constraint by using unpaired data, where the input and output images are not explicitly paired. This method ensures confidentiality, authenticity, and secure transfer. Cycle_GAN consists of two major components: a generator used to modify the images, and a discriminator used to distinguish between real and fake images. Further, the Binary-Cross Entropy loss function is employed to train the network for precise predictions. The experiments are carried out on skin cancer datasets. The results demonstrate high-level efficient, systematic and coherent encryption as compared with other modernized medical image encryption methods. The proposed technique offers several benefits, including efficient encryption and decryption and robustness against unauthorized access.

Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques

Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours.Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes

Data Privacy Preserving for Centralized Robotic Fault Diagnosis With Modified Dataset Distillation

Abstract Industrial robots generate monitoring data rich in sensitive information, often making enterprises reluctant to share, which impedes the use of data in fault diagnosis modeling. Dataset distillation (DD) is an effective approach to condense large dataset into smaller, synthesized forms, focusing solely on fault-related features, which facilitates secure and efficient data transfer for diagnostic purposes. However, the challenge of achieving satisfactory fault diagnosis accuracy with distilled data stems from the computational complexity in data distillation process. To address this problem, this article proposes a modified KernelWarehouse (MKW) network-based DD method to achieve accurate fault diagnosis with the distilled dataset. In this algorithm, DD first generates distilled training and testing dataset, followed by the training of an MKW-based network based on these distilled datasets. Specifically, MKW reduces network complexity through the division of static kernels into disjoint kernel cells, which are then computed as linear mixtures from a shared warehouse. An experimental study based on the real-world robotic dataset reveals the effectiveness of the proposed approach. The experimental results indicate that the proposed method can achieve a fault diagnosis accuracy of 86.3% when only trained with distilled data.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

State of the Art, Reliable, and Trusted Communication in Vehicle to Everything (V2X) Networks

Abstract Transportation systems are a necessity of modern life. To achieve safe and fast transportation, the intelligent transportation system is being developed. In this system, the main base station facilitates communication between vehicles and all other roadside entities using vehicle to everything networks. Various technologies have been built to support this communication. After receiving the information, vehicles can make their own decisions. This data communication is done in real time. But, if any of these data packets are dropped, the vehicle will make inaccurate decisions, so we need to have reliable data communication. All data communication will be accomplished through a system of neighboring nodes. There may be some malfunctioning nodes that lose packets in between, so a trusted and reliable link is necessary for efficient and secure data communication among the nodes. This paper presents a survey of various algorithms designed for the reliable delivery of data packets, algorithms for secure data transfer, and various types of attacks. After that, various schemes for building trust in parties sending data packets if also presented for survey.

SDN architecture‐based secure data transfer with cooperative searching scheme for AUV‐based underwater wireless networks

SDN architecture‐based secure data transfer with cooperative searching scheme for AUV‐based underwater wireless networks

Advancing IOT Interoperability: Dynamic Protocol Translation through Machine Learning For Enhanced Communication Efficiency

This study presents a pioneering methodology for Dynamic Protocol Translation in the Internet of Things (IoT), aiming to overcome challenges posed by diverse communication protocols among IoT devices. The primary objective is to develop a two-fold approach: first, acquiring data from IoT devices through their specific protocols, preprocessing it for consistency, and employing Natural Language Processing (NLP) techniques for semantic extraction and normalization; second, implementing a machine learning model, incorporating neural networks, to discern correlations between normalized representations and target protocol structures. The emphasis is on rigorous testing, validation, & real-time translation capabilities. The main conclusions of the study demonstrate how well the suggested Logistic Regression model performed, with an accuracy of 96.76%, in contrast to an existing model (XML-JSON) that had an accuracy of 82.41%. The detailed evaluation metrics, which include F1 score, precision, and recall, demonstrate how well the suggested method works to solve protocol translation issues. The iterative feedback loop, real-time translation, and secure data transfer of the proposed system improve its adaptability and reliability. This research enhances the field of IoT communication by offering a comprehensive solution for smooth interoperability & communication efficiency in a range of IoT applications.

К ВОПРОСУ ОБЕСПЕЧЕНИЯ СЕТЕВОЙ БЕЗОПАСНОСТИ

The following are considered:  principles and means of ensuring the security of a corporate network, as well as features of virtual private networks that provide reliable protection and secure data transmission;  principles and measures to ensure the security of the corporate network, as well as the types of information protection provided by these security measures;  virtual private networks (VPNs) that provide a secure encrypted user connection to the network. Using a VPN provides reliable data protection, masking the user's geolocation, access to regional content and secure data transfer;  systems for detecting and preventing network hacking threats, among which the main focus is on the Kali Linux software package, as well as features of the Windows subsystem for Linux and Windows network monitoring tools;  Windows network monitoring tools, including utilities that work in console command-line mode and allow you to check the availability of remote personal computers and diagnose the connection. The Kali Linux software package is a distribution package for the Linux operating system that has more than 600 preinstalled network penetration testing programs. Among the features of the package, the article discusses the search and exploitation of network vulnerabilities, checking the correct configuration of the SSL certificate and open ports, tracing the data transfer route, checking the availability of servers and searching for network problems.