Secure Data Transmission Research Articles

Development of a Risk Assessment and Early Warning System for Pharmaceutical Cold Chain Transportation Based on Big Data

Cold chain logistics has become a core link in ensuring drug quality and safety, especially for temperature sensitive drugs such as vaccines and biologics. This paper aims to develop a drug cold chain transportation risk assessment and early warning system based on big data, utilizing advanced technologies such as big data analysis and machine learning to achieve real-time monitoring, intelligent analysis, and early warning of various risk factors in the drug cold chain transportation process. This paper proposes a comprehensive architecture that includes data collection layer, data transmission layer, data processing and storage layer, analysis engine, warning and response module, user interface, and system integration interface. This paper collects key environmental data in real-time through sensors, such as temperature and humidity, and uses a secure data transmission layer to transmit the data to the central processing system. The construction of the risk assessment model is based on a series of quantifiable risk indicators, including temperature deviation, humidity deviation, location deviation, etc., and through data standardization and weight allocation, combined with a machine learning model to perform risk scoring and grading. The early warning mechanism implementation part includes early warning rules and threshold settings as well as real-time monitoring and dynamic warning to ensure that the system can respond to potential risks in a timely manner. The results of the system performance evaluation show that the risk evolution prediction deviation rate remains at a very low level, with a maximum of 1.4%, the false alarm rate is very low, with a maximum of 0.15% and a minimum of 0.03%, and the risk situational awareness sensitivity coefficient remains at a high level, with a minimum of 0.8. These results indicate that the developed system performs well in terms of risk prediction accuracy, warning timeliness and risk perception sensitivity.

Efficient lightweight cryptography for resource-constrained WSN nodes

In Wireless Sensor Networks (WSN), where resources are scarce, lightweight cryptography is crucial and difficult. The Constrained Application Protocol (CoAP), Field-Programmable Gate Array (FPGA) technology, and a modified RC5 algorithm are used in this study to achieve efficient encryption for WSN nodes. WSN are essential for monitoring temperature, humidity, vibrations, and seismic activity. However, processing capacity, memory, and energy constraints make data security difficult in these networks. CoAP, FPGA, and a modified RC5 algorithm provide security, computational load reduction, and energy efficiency in resource-limited WSN. This research is crucial because it creates cryptographic mechanisms that accommodate WSN unique needs. By protecting data integrity and confidentiality, these mechanisms ensure data transmission security and privacy. Data security and efficiency in WSN require innovative cryptographic, FPGA-based hardware acceleration, and protocol optimization solutions. These methods could improve these vital sensing networks. This research lays the foundation for protecting the accuracy and security of WSN node data collected and transmitted with limited resources while optimizing resource allocation.

Optimizing Digital Image Steganography through Hybridization of LSB and Zstandard Compression

In response to the growing need for secure digital communication globally, this research delves into an innovative strategy for enhancing data transmission security through steganography. This inventive approach involves the integration of the conventional Least Significant Bit (LSB) method with Zstandard (Zstd) compression to elevate the quality of stego images. The study carefully explores how the synergistic use of LSB and Zstd contributes to an improved equilibrium between embedding capacity and visual quality in stego images. This hybrid methodology capitalizes on the efficiency of Zstd in reducing file size, thereby facilitating more effective data concealment using LSB. The experimental outcomes showcase a notable 51.2% increase in embedding capacity, a 4.70% elevation in PSNR value, accompanied by a substantial 51.03% decrease in MSE value. Additionally, SSIM values hover around 0.007%, indicating a perceptually minimal difference between the original and steganographically modified images. These compelling results underscore the efficacy of the proposed method, highlighting its proficiency in preserving and enhancing the quality of stego images generated through the embedding process. This research signifies a significant stride in the realm of secure digital communication, demonstrating a promising fusion of traditional LSB with advanced Zstd compression for optimizing digital image steganographic.

Research on power communication data security based on ETL

The use of modern data application technology in information security management programs within power enterprises is a major trend in industry development. Integrate scattered, messy, and non-standard data in electronic communication, extract, clean, and transform business system data, and load it into a data warehouse. ETL mainly relies on cloud computing logic to protect the transfer, storage, confidentiality, and other aspects of data, effectively avoiding the loss, omission, and leakage of information in enterprise data transmission. This study discusses the necessity of ETL based data transmission security protection technology for power enterprises to achieve security, in order to provide theoretical support for subsequent research and practice.

Designing End-to-End Web-Based Application Encryption with Asymmetric Encryption Using Waterfall Methodology

An in-depth exploration of a robust and systematic approach aimed at enhancing the security and integrity of communication systems within a website is conducted in this research. The focus is strengthening the interaction between the server and the user or client. Thus, secure data transmission can be guaranteed. This research integrates the well-established and widely respected waterfall methodology with asymmetric encryption techniques, explicitly using the RSA algorithm, into the overall development process. This method covers data encryption and decryption comprehensively. Blackbox testing validated the results for the application's expectation of increased research reliability. Advanced security measures are integrated into projects that use these insights to protect user data due to fast-growing cyber threats and the importance of data privacy.

Multimedia Data Transmission with Secure Routing in M-IOT-based Data Transmission using Deep Learning Architecture

The proliferation of Multimedia Internet of Things (MIoT) introduces unprecedented challenges in ensuring the security and reliability of data transmission. This paper proposes a novel approach, the Hierarchical Trust Aware Blockchain (HTaB) model, designed to address the complex requirements of MIoT networks. The HTaB model combines the strengths of blockchain technology and a hierarchical trust structure to enhance data integrity, secure routing, and trust management. The integrated blockchain component of the HTaB model is rigorously evaluated, demonstrating the validation of eight blocks per second, a consensus mechanism time of 15 milliseconds, and an average block creation time of 12 seconds. These metrics affirm the efficiency and responsiveness of the blockchain in safeguarding MIoT data transmissions. Simulation results across varying network sizes showcase the scalability of the HTaB model, indicating high throughput, low Latency, and a robust packet delivery ratio. Comparative analyses against Conventional RPL and a Generic Trust Scheme underscore the competitive advantages of the HTaB model, exhibiting superior energy efficiency, higher throughput, a remarkable packet delivery ratio, and minimal packet loss. This paper comprehensively explains the HTaB model’s capabilities in securing and efficiently transmitting multimedia data within MIoT networks. The proposed model presents a promising solution for addressing the evolving challenges in MIoT environments, providing a foundation for further research and practical implementation in real-world scenarios.

IAAP: INTERDEPENDENCY ATTRIBUTE AUTHENTICATION PROTOCOL FOR IoMT SYSTEMS SECURITY ENHANCEMENT AND ANALYSIS

Internet has changed and evolved since its origin and in recent time the communication channel for the internet has developed a flexible system design to accommodate newer devices and customized services. Internet of Medical Things (IoMT) is one such blooming service for larger data processing and customization. In this paper, we propose a novel interdependency attribute protocol for authentication and enhancing security aspects of IoMT communication. The protocol is developed on remote monitoring and Message Queuing Telemetric Transport (MQTT) protocol foundation values to secure data transmission and communication via active public domain internet and cloud storage. The approach is developed on machine learning models for attribute customization and classification. The model has been validated with AWS open pipeline cloud platforms for IoMT devices customization. The IoMT device cloud server evaluation and authentication via interdependency attribute protocol has outperformed the existing security models and analysis with Machine learning tools.

Comparative Analysis of Transport Layer Security (TLS) Versions

Abstract: Transport Layer Security (TLS) serves as a pivotal cryptographic protocol ensuring secure data transmission across networks. This paper conducts a comprehensive comparative analysis spanning TLS versions 1.0 through 1.3. The analysis meticulously examines the evolution of security mechanisms, performance enhancements, identified vulnerabilities, and adoption trends across these iterations. Emphasizing the strengths and weaknesses inherent in each version, this study provides a detailed elucidation of the security advancements and considerations associated with TLS. The aim is to furnish stakeholders, developers, and network administrators with invaluable insights into the nuances of TLS versions, aiding in informed decisionmaking regarding network security implementations. By shedding light on the evolution and varying security features of TLS iterations, this analysis aims to contribute to a deeper understanding of their significance in ensuring robust and secure communication infrastructures

A secure dynamic cross-chain decentralized data consistency verification model

The continuous increase in the number of transactions is exerting significant pressure on the storage capacity of blockchains. By storing data on multiple blockchains and interacting with each other with cross-chain technology, the storage pressure can be relieved to a large degree. Nevertheless, the feature of immutability may impede the further development of blockchains, e.g., some expired identity information cannot be updated due to immutability. The decentralized chameleon hash function can resolve the issue while ensuring the feature of decentralization. Unfortunately, how to ensure the consistency of dynamic data updating in cross-chain interaction is still a question worth considering. In this paper, we propose a dynamic cross-chain decentralized data consistency verification (DCCV) model, in which we adopt an audit chain and design a dynamic Merkle hash tree to guarantee the consistency of dynamic data updating between the source chain and the target chain. In addition, in order to enhance the relevance of calculations across different smart contracts and improve audit efficiency, we propose a way to develop cross-chain smart contracts collaboratively. Moreover, Cosi protocol and multi-signcryption are utilized to ensure the security and privacy of cross-chain data transmission. Finally, theoretical and experimental analysis demonstrates that DCCV can achieve dynamic data consistency verification in the process of cross-chain interaction.

Securing data transmission by cryptography using Rohit integral transform

Securing data transmission by cryptography using Rohit integral transform

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

Hybrid Quantum Key Distribution Protocol with Chaotic System for Securing Data Transmission

This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measurements such as Quantum Bit Error Rate (QBER), Polarization Error Rate (PER), Quantum Fidelity (QF), Eavesdropping Detection (ED), and Entanglement-based detection (EDB), as well as classical cryptographic analysis such as Bit Error Ratio (BER), Entropy, Histogram Analysis, and Normalized Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). As a result, the proposed method obtained satisfactory results, especially perfect QF and BER, and EBD, which reached 0.999.

Software implementation of the data encryption module on the BeagleBone platform for data transmission systems with increased cryptoresistance

In today's digital world, where the exchange of information is an integral part of our daily lives, ensuring information security becomes a criticaltask. This work aimsto develop an information protection module for data encryption on the BeagleBone platform for data transmission systems with increased crypto resistance. Itis a hardware system based on a BeagleBone AI 64 microcomputer with antennas for transmitting/receiving data. Since the information is transmitted over a physically unprotected channel, developing a module that will encrypt the data is necessary. The information protection module ensures the confidentiality of the transmitted data in the system using the AES symmetric encryption algorithm with a variable key length (128/192/256 bits). Regardless of the hardware platform, it is characterized by universality, as it can be run on the Linux kernel adapted for use on embedded systems. Providesoptions for configuring protocols and encryption algorithms. In the process of developing the security module, modern encryption methods and algorithms were used (AES 128/192/256 –for data encryption, RSA –for the distribution of secret keys), in addition, a lightweight protocol for secure data transmission Scplight was implemented as an alternative to OpenSSL, which improves transfer speed on low-power hardware platforms. The developed information protection module has undergone thorough testing on a real system. Thedevelopment of the information protection module is based on the need for a universal component that provides high-quality data protection in wireless communication systems. This module will accelerate the development of relatively affordable physical means of secure communication, a critical partofsuch projects. The module is implemented as a library written in the C language, which implements an API for establishing a secure connection and further forwarding information over an unprotected transmissionchannel. In addition, a configurator programallows you to change the module settings even in real-time when client applications use it. This ensures continuous, seamless, and secure data exchangeandconvenient configuration of the module. Many supporting libraries were used in the development process, including Crypton, Libgcrypt, Openssl, Ncurses and Sqlite3.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Update Algorithm of Secure Computer Database Based on Deep Belief Network

In order to ensure the security of large-scale data transmission in a short time and in a wide range during online database updating, this paper presents a secure computer database updating algorithm based on DBN (Deep Belief Network). In this paper, the model adopts multi-layer depth structure for unsupervised feature learning, maps high-dimensional and nonlinear intrusion data to low-dimensional space, establishes the relationship mapping between high-dimensional and low-dimensional, and then uses fine-tuning algorithm to transform the model to achieve the best expression of features. At the same time, this method improves the data processing and method model without destroying the learned knowledge of the model and seriously affecting the real-time performance of detection. In order to overcome the problem of system instability caused by fixed empirical learning rate, this paper proposes a learning rate optimization strategy based on energy change. In the process of feature extraction, the features of different hidden layers are extracted to form combined features. Experiments show that the detection rate of this method can reach 95.31%, and the false alarm rate is 2.14%. This verifies the effectiveness of the secure computer database updating algorithm in this paper. Which can ensure the online update of the secure computer database.

ENERGY SECURITY MANAGEMENT SYSTEM IN SMALL PORTS

The purpose of the article is to describe a prototype system for managing energy security in small ports based on the use of IT technology. Additional evaluation of the usability of the described sytem will be carried out based on the main pillars of the Industrial Revolution 4.0, which include the integration of advanced information technology (IT), automation (OT), "Internet of Things" (IoT) and artificial intelligence (AI) systems. This affects IT systems, which are becoming more complex and sophisticated all the time. This causes an increase in dependencies - the more complex the information system, the more complex the security must be.In the described system there will be the use of a modular system of hybrid installation of renewable energy sources mainly from wind energy, photovoltaics and biogas, stored in an energy storage. Based on the use of energy storage and management of technology through a secure intelligent information network, the needs for storage and secure transmission of information and data, green electricity production, in accordance with the priorities of the European Union (green energy, green transformation European data strategy) will be described. The article also presents the types of renewable energy sources (RES) in relation to the use of systems supporting their management. The author will also try to answer the question whether such measures will significantly strengthen the energy security of a modern organization.

Security Evaluation and Improvement of the Extended Protocol EIBsec for KNX/EIB

The European Installation Bus(EIB) protocol, also known as KNX/EIB, is widely used in building and home automation. An extension of the KNX/EIB protocol, EIBsec, is primarily designed to meet the requirements for data transmission security in distributed building automation systems. However, this protocol has some security issues in the request, key distribution, and identity authentication processes. This paper employs a formal analysis method that combines Colored Petri Net (CPN) theory with the Dolev-Yao attack model to evaluate and enhance the EIBsec protocol. It utilizes the CPN Tools to conduct CPN modeling analysis on the protocol and introduces a security assessment model to carry out intrusion detection and security assessment. Through this analysis, vulnerabilities in the protocol, such as tampering and replay attacks, are identified. To address these security concerns, we introduce hash verification and timestamp judgment methods into the original protocol to enhance its security. Subsequently, based on the improved protocol, we conduct CPN modeling and verify the security of the new scheme. Finally, through a comparison and analysis of the performance and security between the original protocol and the improved scheme, it is found that the improved scheme has higher security.

Social media platform-oriented topic mining and information security analysis by big data and deep convolutional neural network

The aim of this work is to conduct topic mining and data analysis of social network security using social network-based big data. The deep convolution neural network (DCNN) is used to analyze social network security issues. Traditional neural network models cannot handle long sequence data when extracting information on Weibo security topics. Thus, the long short-term memory (LSTM) structure in the memory intelligence algorithm extracts Weibo topic information. Specifically, the social network security topics are mined through Big Data, and CNN searches Weibo security topics. CNN can learn the grammar and semantic information of Weibo topics to obtain in-depth data features. Afterward, the performance of the improved DCNN model is compared with the AlexNet, Convolutional Neural Network (CNN), and Deep Neural Network (DNN) by considering the model's accuracy, recall, and F1 value, respectively. The experimental results show that after 120 iterations, the recognition accuracy of the improved DCNN model peaks at 96.17 %, at least 5.4 % superior to the other three models. Additionally, the intrusion detection model's accuracy, recall, and F1 value are 88.57 %, 75.22 %, and 72.05 %, respectively. In the worst case, the constructed model's accuracy, recall, and F1 value are 3.1 % higher than those of the other methods. The training and testing time consumption of the improved DCNN security detection model stabilized at 65.86 s and 27.90 s, much shorter than similar literature approaches. The experimental conclusion is that the improved DCNN under deep learning has the characteristic of lower delay, and the model shows good network data security transmission.

Full-round impossible differential attack on shadow block cipher

Lightweight block ciphers are the essential encryption algorithm for devices with limited resources. Its goal is to ensure the security of data transmission through resource-constrained devices. Impossible differential cryptanalysis is one of the most effective cryptanalysis on block ciphers, and assessing the ability of resisting this attack is a basic design criterion. Shadow is a lightweight block cipher proposed by Guo et al. (IEEE Internet Things J 8(16):13014–13023, 2021). It utilizes a combination of ARX operations and generalized Feistel structure to overcome the weakness of the traditional Feistel structure that only diffuses half in one round. In this paper, we focus on the differential property of Shadow and its security against impossible differential cryptanalysis. First, we use the SAT method to automatically search for a full-round impossible differential distinguisher of Shadow-32. Then, based on the experimental results, we prove that Shadow has a differential property with probability 1 based on the propagation of the state. Further, we can obtain an impossible differential distinguisher for an arbitrary number of rounds of Shadow. Finally, we perform a full key recovery attack on the full-round Shadow-32 and Shadow-64. Both experimentally and theoretically, our results indicate that Shadow is critically flawed, and regardless of the security strength of the internal components and the number of rounds applied, the overall cipher remains vulnerable to impossible differential cryptanalysis.

An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model

The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction.