Secure Data Transmission Research Articles

Bio-Inspired algorithms for secure image steganography: enhancing data security and quality in data transmission

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images.

Enhancing Trust and Security in IoT Architecture for Low-Cost Microcontroller Devices using Elliptic Curve Cryptography

Abstract—In the rapidly expanding universe of the Internet of Things (IoT), low-cost microcontroller devices, such as the NodeMCU, have become ubiquitous. While these devices have transformed how we interact with the physical world, they also introduce significant security vulnerabilities. Chief among these is susceptibility to man-in-the-middle (MITM) attacks, which pose a serious risk to the confidentiality and integrity of data transmitted across these networks. This study addresses this critical issue by proposing the adoption of Elliptic Curve Cryptography (ECC), a cryptographic method known for its efficiency and strong security, despite the computational limitations of low-cost IoT devices. We conducted an experiment demonstrating the ease with which an MITM attack can intercept data from a NodeMCU device. Following this, we implemented ECC to secure data transmission, showcasing its viability as a lightweight yet robust security solution. Our research not only highlights the pressing need for enhanced security measures in the IoT ecosystem but also provides a practical framework for securing low-cost microcontroller devices against sophisticated cyber threats. Through our findings, we contribute to the development of more secure, trustworthy IoT architectures, ensuring that these devices can continue to safely serve as integral components of our digital lives. Keywords—NodeMCU, ESP8266, Data theft, Elliptic Curve Cryptography, Man-in-the-middle attack

Application of Data Mining Combined with K-means Clustering Algorithm in Enterprises' Risk Audit

The financial risk management mechanism of enterprises can be more complete through exploration in the application effect of data mining technology combined with K-means clustering algorithm in enterprise risk audit. Hence, K-means clustering algorithm is introduced to study the paperless status of electronic payment in the trading process of e-commerce enterprises. Additionally, a risk audit model of e-commerce enterprises is implemented based on K-means algorithm combined with Random Forest Light Gradient Boosting Machine (RF-LightGBM). In this model, the actual operation process of data preparation, data preprocessing, model construction, model application and evaluation are implemented to study the payment flow in the transaction process of e-commerce enterprises by using big data analysis technology. Eventually, the performance of the model is evaluated by simulation. The results show that, compared with the models and algorithms proposed by scholars in other related fields, the classification accuracy of the model proposed here reaches 95.46 %. Simultaneously, the data message delivery rate of the model algorithm is basically stable at about 81.54 %, and the data message leakage rate, packet loss rate and average delay are lower than those of other models and algorithms. Therefore, under the premise of ensuring the prediction accuracy, the audit model of e-commerce enterprises can also achieve high data transmission security performance, which can provide experimental basis for the safety improvement and risk control of the audit process in e-commerce enterprises.

Physical layer security analysis for RIS-aided NOMA systems with non-colluding eavesdroppers

Within the realm of sixth-generation (6G) wireless systems, there exist two primary imperatives: establishing massive connections and ensuring robust data transmission security. Therefore, this paper delves into the realm of physical layer security (PLS) within the context of a reconfigurable intelligent surface (RIS)-assisted Non-Orthogonal Multiple Access (NOMA) network coupled with the Internet of Things (IoTs), while addressing the challenge of non-concluding eavesdroppers. Specifically, the utilization of NOMA technology is anticipated to yield a substantial enhancement in spectrum efficiency for 6G and forthcoming wireless networks. Furthermore, this study investigates the security aspects through metrics such as the secrecy outage probability (SOP) and the average secrecy capacity (ASC), with the derivation of closed-form approximations for these metrics. Based on these mathematical expressions, we unveil the asymptotic Secrecy Outage Probability (SOP) to extract comprehensive insights into the RIS-assisted NOMA system’s behavior. Furthermore, we employ an algorithm based on the Golden Section to showcase the optimal SOP for a more in-depth analysis. Our findings highlight that the number of RIS metasurface components and the average signal-to-noise ratio at the access point are the primary factors driving improvements in system performance. Finally, we confirmed the correctness of our derived expressions by conducting a comparative analysis between Monte-Carlo simulations and analytical results.

An Energy-Efficient Cluster Head Selection and Secure Data Transmission in WSN using Spider Monkey Optimized Algorithm and Hybrid Cryptographic with Security

To conserve energy in wireless sensor networks, clustering is the well-known strategies. However, choosing a cluster head that is energy efficient is crucial for the best clustering. Because data packets must be transmitted between cluster members and the sink node, improper cluster head selection (CHs) uses more energy than other sensor nodes. As a result, it lowers the network's performance and lifespan. Due to the requirement that this network implement appropriate security measures to guarantee secure communication. This paper provides a novel cluster head selection technique that addresses issues of networks’ lives and energy usages using Spider Monkey Optimised Fuzzy C-Means Algorithm (SMOFCM). The CH is chosen using the Spider Monkey Optimisation method in the proposed SMOFCM approach, which builds on the Fuzzy C-means clustering framework. The hybrid cryptographic technique is appropriate for WSN for safe data transmission because it can address sensor challenges such processing power, storage capability, and energy. The Rivest-Shamir-Adleman (RSA), advanced encryption standards (AES), and the suggested algorithm are all used at various stages. Because asymmetric key cryptography makes key management simpler but symmetric key cryptography offers a high level of security. The AES algorithm has been created for phase 1. Phase 2 employed RSA, and all phases were carried out concurrently. According to the simulation results, it reduces energy use, lengthens the network's lifespan, and offers faster encryption, decryption, and execution times for secure data transmission

Leveraging Intermolecular Charge Transfer for High-Speed Optical Wireless Communication.

Intermolecular charge transfer (CT) complexes have emerged as versatile platforms with customizable optical properties that play a pivotal role in achieving tunable photoresponsive materials. In this study, we introduce an innovative approach for enhancing the modulation bandwidth and net data rates in optical wireless communications (OWCs) by manipulating combinations of monomeric molecules within intermolecular CT complexes. Concurrently, we extensively investigate the intermolecular charge transfer mechanism through diverse steady-state and ultrafast time-resolved spectral techniques in the mid-infrared range complemented by theoretical calculations using density functional theory. These intermolecular CT complexes empower precise control over the -3 dB bandwidth and net data rates in OWC applications. The resulting color converters exhibit promising performance, achieving a net data rate of ∼100 Mb/s, outperforming conventional materials commonly used in the manufacture of OWC devices. This research underscores the substantial potential of engineering intermolecular charge transfer complexes as an ongoing progression and commercialization within the OWC. This carries profound implications for future initiatives in high-speed and secure data transmission, paving the way for promising endeavors in this area.

Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using Bioinspired Algorithms (EKD-SOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS-TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

3S-ALDDT: A Triple Security Scheme for Authenticating Lightweight Devices and Securing Data Transmission in Internet of Things

3S-ALDDT: A Triple Security Scheme for Authenticating Lightweight Devices and Securing Data Transmission in Internet of Things

FCMPR:A multi-path secure transmission method based on link security assessment and fountain coding

The security of network data transmission has always been a focus of attention. With the rapid development of quantum computers and the rise of intelligent algorithms such as semantic analysis, traditional data encryption methods struggle to ensure the secure transmission of data. The avoidance routing protocol improves transmission security by circumventing malicious nodes and dangerous areas, but it misses the actual optimal path setup, reducing routing opportunities and increasing communication delays, thus failing to meet the application requirements that demand high security and low latency. To address these issues, this paper proposes a multi-path secure transmission method (FCMPR) based on link security assessment and fountain coding. We have designed a link security assessment method based on a random forest traffic detection model, which can obtain the link's confidence level to assess its security. Furthermore, we have proposed a secure multi-path routing transmission mechanism based on fountain code, enabling safe and rapid data transmission without avoiding malicious nodes. Experimental results show that even with a high proportion of malicious nodes in the network, FCMPR can significantly improve the data transmission rate, reduce the transmission delay, and enhance communication quality while ensuring data security.

SECURING AND ADVANCING ROAD SAFETY IN INTELLIGENT VEHICULAR NETWORKS FOR SEAMLESS AND SECURE COMMUNICATION

Intelligent vehicular networks leverage communication technologies to enable vehicles to interact with each other and with infrastructure elements, such as roadside units and traffic signals, to enhance road safety and efficiency. However, the open nature of these networks exposes them to various security threats, including data interception, tampering, and unauthorized access. Traditional encryption methods may not suffice to address these challenges, necessitating the adoption of advanced cryptographic techniques like ECC and RSA. Prior research in the field has primarily focused on either enhancing communication protocols or improving security measures independently. However, there is a notable gap in research that comprehensively addresses both aspects simultaneously. This study aims to fill this void by proposing an integrated approach that ensures both seamless communication and robust security in intelligent vehicular networks. The proposed methodology involves the design and implementation of a hybrid cryptographic scheme combining ECC and RSA algorithms. This scheme will be integrated into the existing communication infrastructure of intelligent vehicular networks. The performance of the system will be evaluated through simulations and real-world experiments to assess its effectiveness in securing communication channels while minimizing overhead. The results show the effectiveness of the proposed ECC-RSA hybrid encryption scheme in securing communication channels within intelligent vehicular networks. The integration of ECC and RSA protocols not only enhances the security of data transmission but also ensures seamless communication, thereby advancing road safety in intelligent vehicular environments.

S2Cloud: a novel cloud-based precision health system for smart and secure IoT big data harnessing

This study aims to demonstrate a novel cloud big data system, s2Cloud, to enabled both smart and secure transmission and management of patient big data from Internet of Things (IoT), especially in the new common of long covid. More specifically, s2Cloud allows doctors to manage patient records effectively through the interactive web server. It further enables real-time, long-term, and continuous streaming and management of biomechanical dynamics from IoT phones/wearables. The big data can be visualized in both real-time and historical modes. Besides, s2Cloud achieves big data security through secure sign up and log in, as well as data transmission protection. It also allows doctor-patient interactions through instantaneous chat. s2Cloud has been evaluated on the real-world application, in which human IoT biomechanical dynamics are streamed, managed, stored, and visualized to demonstrate the effectiveness. Overall, the proposed system, empowered by smart and secure design innovations, has demonstrated the feasibility and potential for IoT big data-driven precision health.

Information Technology Security and Privacy Protection in Vehicular Networks (VANETs)

Abstract: As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and IT Security is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the IT Security vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and IT Security hurdles.

Enhancing Security in Vehicle-to-Vehicle Communication: A Comprehensive Review of Protocols and Techniques

Vehicle-to-vehicle (V2V) communication has played a pivotal role in modern intelligent transportation systems, enabling seamless information exchange among vehicles to enhance road safety, traffic efficiency, and overall driving experience. However, the secure transmission of sensitive data between vehicles remains a critical concern due to potential security threats and vulnerabilities. This research focused on investigating the security protocols that have been employed in vehicle-to-vehicle communication systems. A comprehensive review and analysis of relevant literature and research papers was conducted to gather information on existing V2V communication security protocols and techniques. The analysis encompassed key areas, including authentication mechanisms, encryption algorithms, key management protocols, and intrusion detection systems specifically applicable to V2V communication networks. Within the context of real-world V2V environments, this study delved into the challenges and limitations associated with implementing these protocols. The research aimed to provide a comprehensive understanding of the strengths and weaknesses of the current V2V communication security protocols. Furthermore, based on the findings, this paper proposes improvements and recommendations to enhance the security measures of the V2V communication protocol. Ultimately, this research contributes to the development of more secure and reliable V2V communication systems, propelling the advancement of intelligent transportation technology.

Application of AES and 3DES Hybrid Encryption Algorithm Based on Optimized S Box in the Data Transmission of Human Resources and Social Security Informatization

In this paper, a hybrid encryption algorithm based on AES and 3DES is proposed to improve the security of data transmission in human resources and social security informatization. We first analyze the advantages and disadvantages of AES and 3DES, and then elaborate on why the two algorithms are used in combination. Then, this paper discusses the implementation process and mathematical principle of the hybrid algorithm. Finally, we implement the hybrid algorithm in the C + + language and present an application example.

Energy efficient routing and secured data transmission in the IoV: Improved deep learning model for energy prediction

Energy efficient routing and secured data transmission in the IoV: Improved deep learning model for energy prediction

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange.

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach's effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.

Enhancing wireless sensor network security and efficiency with CNN-FL and NGO optimization

In this study, we discuss how Wireless Sensor Networks (WSN) are susceptible to malicious assaults from malfunctioning nodes, which jeopardise the security of data transmission in crucial applications. We use a Convolutional Neural Network coupled with Fuzzy Logic (CNN-FL) for improved deep learning, introducing a unique method, effectively identifying and categorizing trustworthy and malicious nodes. This is combined with a routing strategy enhanced by the Neuro Genetic Optimizer (NGO), built on the LEACH routing protocol and based on a Roulette wheel selection mechanism. The WSN's lifetime is increased by our suggested routing method, which not only provides safe data transmission but also dramatically reduces latency and energy consumption. Simulation findings show that our technique outperforms current protocols as ASNGSRA, DMCNN, and FRCSROD in terms of packet delivery ratio, energy economy, and latency analysis.

A secure and efficient UAV network defense strategy: Convergence of blockchain and deep learning

Unmanned Aerial Vehicles (UAVs) are highly versatile and efficient tools utilized across diverse industries for data collection. However, they are vulnerable to wireless communication and data exchange risks, including unauthorized access, data theft, and network attacks. To address these problems, we introduce a secure and reliable UAV network service architecture that incorporates blockchain and deep learning to provide more secure and efficient network services for UAVs. We propose a UAV cluster identity management module by combining blockchain, encryption algorithms, and digital signatures to enhance the security of UAV communication data transmission. Then, based on machine learning, deep learning, and malicious process detection technology, we propose a real-time secure situational awareness system for UAV cluster terminal devices to enhance the security of the operating environment for UAVs. Finally, we propose a data-trustworthy interconnection platform based on blockchain, smart contracts, and consensus algorithms to realize secure and efficient sharing and transmission of terminal data. The results of the experiments demonstrate the feasibility and effectiveness of our UAV network service architecture.

A review of IoT security and privacy using the Blockchain to Boost Data Integrity and Privacy

The Internet of Things (IoT) is an emerging technology. It enables users to exchange their data and resources by linking devices across the IoT network, resulting in a more relaxed and connected lifestyle. It allows real-time data to be delivered to end-user applications and systems via sensing, actuation, communication, computing, networking, and storage. It can transfer data across a network without requiring human intervention or computer-to-computer interaction. It makes it possible to detect the various sensors around the world through low-priced sensor devices. IoT Networking defines communication or inter-connection between Peer-to-Peer (P2P) either through a connected medium (Wired) or unconnected medium (Wireless) to procure a secure transmission of data. Blockchain is a peer-to-peer network where peers can communicate and do transactions without the need for centralized authority. It allows data to be stored and transferred in a safe and interconnected method. This section includes a literature review on the centralized IOT system model, highlighting the limitations of the centralized IoT system and blockchain security in IoT. This research study's systematic evaluation is processed based on the reviews of the literature available with recent advancements and inventions through patents, articles, conference papers, and journals. Also, the related work with IoT systems for data privacy and security issues and applying Blockchain technology to IOT are discussed.

SAC-based UAV mobile edge computing for energy minimization and secure data transmission

In recent years, the use of UAVs to carry mobile edge computing servers has become an emerging solution to the problem of collaborative ground-to-air communication. However, due to the limited energy consumption of UAVs and the open nature of wireless communication, UAV-based mobile edge computing faces issues such as limited operation time and the potential for insecure connections during data transmission. In this paper, we model a secure data transmission model for UAVs with the goal of minimizing energy consumption. First, we decompose the system energy consumption optimization problem into two sub-problems: ground user energy consumption overhead and UAV energy consumption overhead. We consider the limitations of UAV flight range, computational power, and transmission capability in relation to ground user and UAV, solve the energy consumption optimization problem by deep reinforcement learning, and propose a solution based on the SAC algorithm. The solution applies the idea of maximum entropy to explore the optimal policy and use efficient iterative updates to obtain the optimal policy, enhancing the exploration capability of the algorithm and improving the convergence speed of the training process by retaining all policies with high return values. Secondly, considering the existence of insecure connection nodes in practical application scenarios, a ground user data protection scheme based on Paillier encryption and blockchain is used to achieve secure data transmission. Simulation results show that the proposed method can achieve secure data offloading, effectively reduce the average energy consumption of users, and have good stability and convergence compared with existing methods.