Secure Data Transmission Research Articles

HCFAIUN: A novel hyperelliptic curve and fuzzy extractor-based authentication for secure data transmission in IoT-based UAV networks

IoT-based UAV networks comprise interconnected UAVs outfitted with sensors and microcontrollers to simplify data exchange in environments such as smart cities. In light of open-access communication landscapes, IoT-based UAV networks could pose security challenges, encompassing authentication vulnerabilities and the inadvertent disclosure of location and other confidential information to unauthorised parties. Henceforth, we have proposed a lightweight and secure authentication protocol: Hyperelliptic Curve and Fuzzy Extractor based Authentication in IoT-based UAV networks (HCFAIUN) leveraging Hyperelliptic Curve Cryptography(HCC), Fuzzy Extractor (FE), XOR operations and hash functions. HCC's maximum key size is 80 bits, differing from the 160-bit requirement of the elliptic curve, making it apt for UAVs with limited resources. The proposed scheme utilises biometrics traits of users to avoid exposing data from stealing smart devices using FE. This protocol facilitates the mutual authentication of users and UAVs, allowing them to exchange a session key for secure communication. The Hyperelliptic Curve (HC) scalar multiplication protects the user's private key from attackers, even in public channels. The obfuscation identity of the user and UAVs generated through the hash function and timestamp makes the external user and UAV anonymous. The efficacy of this proposed framework is examined using the Scyther verification tool and Random oracle model-based formal analysis, and informal analysis is also discussed, which validates its robustness against well-known potential physical and logical attacks. The performance analysis shows that the HCFAIUN scheme has lower computation, communication, and storage costs, i.e., 3.832 ms and 1456 bits and 1128 bits, respectively, compared to existing schemes.

Aquila Optimized Fuzzy Deep Belief Network for Secure Data Transmission in WSN

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications.

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

Binary Encoding-Based Federated Learning for Traffic Sign Recognition in Autonomous Driving

Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is urgent to introduce a distributed machine learning approach to protect private data of connected vehicles. In this paper, we propose a local differential privacy-based binary encoding federated learning approach. The binary encoding techniques and random perturbation methods are used in distributed learning scenarios to enhance the efficiency and security of data transmission. For the vehicle layer in this approach, the model is trained locally, and the model parameters are uploaded to the central server through encoding and perturbing. The central server designs the corresponding decoding, correction scheme, and regression statistical method for the received binary string. Then, the model parameters are aggregated and updated in the server and transmitted to the vehicle until the learning model is trained. The performance of the proposed approach is verified using the German Traffic Sign Recognition Benchmark data set. The simulation results show that the convergence of the approach is better with the increase in the learning cycle. Compared with baseline methods, such as the convolutional neural network, random forest, and backpropagation, the proposed approach achieves higher accuracy in the process of traffic sign recognition, with an increase of 6%.

Design and Analysis of a Novel Fractional-Order System with Hidden Dynamics, Hyperchaotic Behavior and Multi-Scroll Attractors

The design of chaotic systems with complex dynamic behaviors has always been a key aspect of chaos theory in engineering applications. This study introduces a novel fractional-order system characterized by hidden dynamics, hyperchaotic behavior, and multi-scroll attractors. By employing fractional calculus, the system’s order is extended beyond integer values, providing a richer dynamic behavior. The system’s hidden dynamics are revealed through detailed numerical simulations and theoretical analysis, demonstrating complex attractors and bifurcations. The hyperchaotic nature of the system is verified through Lyapunov exponents and phase portraits, showing multiple positive exponents that indicate a higher degree of unpredictability and complexity. Additionally, the system’s multi-scroll attractors are analyzed, showcasing their potential for secure communication and encryption applications. The fractional-order approach enhances the system’s flexibility and adaptability, making it suitable for a wide range of practical uses, including secure data transmission, image encryption, and complex signal processing. Finally, based on the proposed fractional-order system, we designed a simple and efficient medical image encryption scheme and analyzed its security performance. Experimental results validate the theoretical findings, confirming the system’s robustness and effectiveness in generating complex chaotic behaviors.

A Self-Sovereign Identity Privacy-Preserving Scheme for Logistics Transportation Based on One-Time-Use Tokens

The advancement of the logistics industry has fostered the enhancement of operational efficacy within the socioeconomic domain. However, the current inevitable privacy leaks in the process of logistics transportation have seriously affected the development of the industry, which led to a crisis of consumer trust and even caused economic recession. This paper proposes a self-sovereign identity privacy protection scheme tailored specifically for logistics transportation contexts. First, the scheme entails furnishing users with one-time-use tokens while establishing decentralized identities capable of concealing identity information and ensuring the secure transmission of data. Furthermore, the scheme integrates fuzzy identity-based encryption to encrypt identity information, thereby guaranteeing the confidentiality and integrity of logistics user identities along with their associated data. Compared with other schemes, this scheme exhibits superior security in the realm of logistics transportation. Its targeted encryption technology and self-sovereignty strategies address the critical issue of privacy leakage, thereby safeguarding consumer privacy rights and interests while facilitating the sustainable development of the logistics industry.

Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain

AbstractAirport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.

Engineering Autonomous Mobility: Wired and Wireless CAN Bus Design Principles

<div>The controller area network (CAN) bus, the prevailing standard for in-vehicle networking (IVN), has been used for more than four decades, despite its simple architecture, to establish communications between electronic control units (ECUs). Weight, maintenance overheads, improved flexibility, and wiring complexity escalate as the quantity of ECUs rises, especially for high-demand autonomous vehicles (AVs). The primary objective of this study is to examine and discuss the significant challenges that arise during the migration from a wired CAN to a wireless CAN (WCAN). Suggested remedies include changing the configuration of the conventional ECU, creating a hidden wireless communication domain for each AV, and developing a plan to counteract the jamming signals. The simulation of the proposed WCAN was done using MATLAB and validated using OPNET analysis. The results showed that the packet loss of the eavesdropping electronic control unit ranged from 63% to 100%. Anti-jamming results show that when packet loss reaches 2% for a continuous period of time of 0.01 sec, the passive channel is automatically activated, ensuring secure data transmission.</div>

IoT-based trusted wireless communication framework by machine learning approach

The traditional Radio-Frequency Systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system.

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.

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Color Generation and Polarization-Sensitive Encryption by Laser Writing on Plasmonic Reflector Arrays.

Plasmonic color printing presents a sustainable solution for vibrant and durable color reproduction by leveraging the light-manipulating properties of nanostructures. However, the fabrication of plasmonic nanostructures has posed challenges, hindering widespread adoption. In this paper, we introduce plasmonic reflector arrays (PRAs) composed of three layers─Ag nanoparticles (NPs), an Al2O3 spacer, and an Ag reflector─deposited via physical vapor deposition (PVD). By employing nanosecond and femtosecond laser writing techniques, we manipulate the surface morphology of silver nanoparticles on PRAs, resulting in a diverse range of structural colors that are both polarization-insensitive and polarization-sensitive. Furthermore, we demonstrate the versatility of nanosecond laser writing in creating intricate patterns on PRAs. Additionally, we propose a novel two-step method combining nanosecond and femtosecond laser processing to embed QR code patterns into PRAs, showcasing their potential for secure data encryption and transmission. This research underscores the promising applications of PRAs in advanced color printing and secure optical data encoding.

Flexo-printed polymer waveguides for integration in electro-optical circuit boards

Electro-optical circuit boards (EOCBs) offer great potential for short-ranged data transmission in highly electro-magnetic inflicted environments. Finding a cost-efficient way to manufacture EOCB only using additive printing processes could establish, increase, and secure data transmission in PCB systems. Flexo printing is an efficient manufacturing process that combines high contour resolution and layout flexibility to create optical waveguides. Previous research has shown that printing waveguides on a polymethylmethacrylate substrate can enable optical data transmission for up to 20 cm. However, a thermo-resistant polyimide (PI) substrate is needed to integrate printed waveguides into PCB. Since PI does not meet optical demands, waveguide cores must be separated by printed optical cladding. This research aims to investigate the additive printing process, which stacks various polymers to achieve waveguides that are ready for integration. Further, the integration in PCB is validated according to functional testing of the optical structures. An entire manufacturing process for printed EOCB is presented, which enables the investigation of optical coupling processes in upcoming research.

Research and Application of a Generalized Power Communication Framework with Joint Security Protection Mechanism

Background: The rapid development of Industry 4.0 has opened up new power systems, which face a more complex network topology and a broader range of security threats. This has led to an urgent demand for a coordinated, lightweight, and secure communication management method. Methods: This paper presents a general power communication framework with a joint security protection mechanism that is suitable for coordinated management and transmission safety. The framework manages the protocol’s thread management of the device by converting it to the framework’s process management. It formulates specifications for protocols and equipment that access the framework to achieve support for multiple protocols and equipment. This reduces the difficulty of system operation and maintenance, as well as system complexity, and improves management efficiency. Additionally, the joint security protective mechanism for plugin authentication and protocol encryption is introduced to enable secure data transmission and guarantee the framework's security management. Finally, two experiments have been conducted to discuss and confirm the effectiveness and safety of the framework. Results: The results of the two experiments and the following analysis show that our framework is superior to the chosen well-known industrial commercial communication platforms, and the time delay cost by our mechanism doesn’t significantly affect the real-time data transmission in the power industry, proving its feasibility in both IoT and power applications. Conclusion: Our framework demonstrates exceptional performance in terms of time lag and packet loss during the experiment, surpassing other IoT communication frameworks. Furthermore, it maintains its high standards even under rigorous security analysis.

Robust and secure data transmission using artificial intelligence techniques in Ad-Hoc networks

Robust and secure data transmission using artificial intelligence techniques in Ad-Hoc networks

Security and Privacy for Smart Transportation Management using Big Data Analytics

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

FPGA Implementation of 256-Bit Key AES Algorithm

Abstract: The most popular network security technique for secure data transmission between two end users, particularly over a public network, is the AES algorithm, also known as the Rijndael algorithm. It may be found in all kinds of wired and wireless digital communication networks AES, a network security technique, uses 256 bits on an FPGA. An authorized cryptographic technique that can be used to secure electronic data is the Advanced Encryption Standard technique (AES), which is specified by the National Institute of Standards and Technology. Because they provide faster and more flexible solutions, reprogrammable devices like Field Programmable Arrays (FPGA) are a very appealing alternative for hardware implementation of the encryption algorithm AES. The effective FPGA implementation of advanced encryption standards (AES) is proposed in this research. The encryption code is written in the Verilog programming language. The AES Rijndael algorithm performs better than any other when implemented on FPGA. Other works are covered by this implementation to demonstrate its effectiveness. The block and key sizes in the design are 256, and an iterative looping technique is used. Bits, S-box implementation using lookup tables. This results in a low-complexity design that can readily achieve high throughput and low latency

Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm

A smart healthcare system (SHS) is a health service system that employs advanced technologies such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information and connect people and institutions related to healthcare, thereby actively managing and responding to medical ecosystem needs. Edge computing (EC) plays a significant role in SHS as it enables real-time data processing and analysis at the data source, which reduces latency and improves medical intervention speed. However, the integration of patient information, including electronic health records (EHRs), into the SHS framework induces security and privacy concerns. To address these issues, an intelligent EC framework was proposed in this study. The objective of this study is to accurately identify security threats and ensure secure data transmission in the SHS environment. The proposed EC framework leverages the effectiveness of Salp Swarm Optimization and Radial Basis Functional Neural Network (SS-RBFN) for enhancing security and data privacy. The proposed methodology commences with the collection of healthcare information, which is then pre-processed to ensure the consistency and quality of the database for further analysis. Subsequently, the SS-RBFN algorithm was trained using the pre-processed database to distinguish between normal and malicious data streams accurately, offering continuous monitoring in the SHS environment. Additionally, a Rivest-Shamir-Adelman (RSA) approach was applied to safeguard data against security threats during transmission to cloud storage. The proposed model was trained and validated using the IoT-based healthcare database available at Kaggle, and the experimental results demonstrated that it achieved 99.87 % accuracy, 99.76 % precision, 99.49 % f-measure, 98.99 % recall, 97.37 % throughput, and 1.2s latency. Furthermore, the results achieved by the proposed model were compared with the existing models to validate its effectiveness in enhancing security.

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Correction to: Certificate-less Aggregate Signature Authentication Scheme (CLASAS) for secure and efficient data transmission in Wireless Sensor Networks (WSNs)

Search for high-probability differential characteristics of the lightweight block cipher algorithm present with non-standard substitution blocks

The development of the Internet of Things and the associated devices has made it necessary to establish and implement encryption standards to ensure secure data transmission. These standards need to be comply with fundamental encryption principles and cater to devices with limited computational resources. As a result, lightweight cryptography has emerged as a distinct field within cryptography. The PRESENT block cipher algorithm is a lightweight encryption algorithm designed for deployment in resource-constrained devices. It requires comprehensive and ongoing vulnerability analysis against both known and novel cryptanalysis methods. This work extensively investigates the PRESENT block cipher algorithm, examining its components, operational principles, and key scheduling algorithm. This study analyses existing research on the algorithm with regards to contemporary cryptanalysis methods. Differential cryptanalysis was selected as the method of choice. The requirements for constructing S-boxes, as set forth by the algorithm developers, are reviewed. Two alternative S-boxes are formulated and presented based on these requirements. The paper presents a methodology for identifying high-probability differential characteristics for the PRESENT algorithm, using a substitute substitution block that differs from the one proposed by the developers. The research reports on the encryption algorithm PRESENT, using alternative substitution blocks, and evaluates its resistance to differential cryptanalysis. The text presents the results of applying the methodology for searching differential characteristics to the substituted blocks in the PRESENT algorithm. A comparative analysis is made between the results obtained through the differential characteristic search methodology for the PRESENT algorithm with alternative substitution blocks and the known results for this algorithm.