Secure Data Transmission Research Articles

Performance Evaluation of Laguerre Transform and Neural Network-based Cryptographic Techniques for Network Security

As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up less memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making the LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic algorithm.

Robust Encryption Framework for IoT Devices Based on Bit-plane Extraction, Chaotic Sine Models, and Quantum Operations

In the domain of Internet of Things (IoT) applications, quantum computers offer the promise of addressing complex computational issues that traditional cryptographic methods find challenging. The unparalleled computational power of quantum computers makes existing cryptographic schemes, tailored to mitigate computational complexity, ineffective. This research introduces a robust encryption scheme designed specifically for digital data exchanged between IoT devices such as smartphones, smart sensors, and smart health monitoring devices. The proposed encryption framework is based on bit-plane extraction-based encryption, the chaotic sine model, the hyperchaotic map, the quantum baker map, and quantum operations such as quantum selective scrambling. The proposed methodology involves three key phases: the first and last phase related to permutation, which is performed at the bit level based on discrete bit-plane scrambling, bit-plane extraction, and hyperchaotic mapping. However, the second phase is based on diffusion, which depends on the chaotic sine model. During the initial stage, creating correlations among image pixels, along with a couple of chaotic sequences using a quantum baker map, is accomplished through a superposition of quantum-states. Before applying diffusion, the pixels of the grayscale image are encoded using a single qubit, and quantum operations are based on a quantum circuit featuring swap gates. To introduce pixel-level permutation, a hyperchaotic map generates random sequences within the range of 0 to 255. A circular shift further shuffles the pixel rows and pixel columns of the permuted image pixels. To enhance the digital image security further, bit-planes are extracted from the permuted bit-planes. Then, the bits within each bit plane are scrambled using a random sequence generated by the hyperchaotic map. Finally, the encoded bit planes are merged to produce a final encrypted image. Experimental results demonstrate the effectiveness of the proposed methodology, with security parameters such as correlation, entropy, and key space measuring at 0.0001, 7.999, and >2100, respectively. Furthermore, to assess performance in relation to computational complexity, a detailed analysis of computational time is conducted. The results reveal that the proposed encryption scheme can execute all encryption steps presented in the proposed method in less than one second. These findings demonstrate the secure transmission of digital data between IoT devices and show the suitability of the proposed encryption scheme for real-time applications.

Enhancing Data Security through Machine Learning-based Key Generation and Encryption

In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations.

Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction Using Blockchain Technology.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.

An Efficient Lightweight Crypto Security Module for Protecting Data Transmission Through IOT Based Electronic Sensors

The Internet of Things (IoT) devices are advanced nanoelectronics devices which has recently witnessed an explosive expansion in the field of communication and electronics, becoming ubiquitous in various applications. However, the rapid growth of IoT applications makes them prone to security threats and data breaches. Hence, cryptographic techniques are developed to ensure data confidentiality and integrity in IoT and many of the applications from optoelectronics. However, the existing cryptographic algorithms face challenges in securing the data from threats during transmission, as they lack effective key management. Therefore, we proposed a novel optimized lightweight cryptography (LWC) to resolve this challenge using the combined benefits of Grey Wolf Optimization and Hyper Elliptic Curve Cryptography (GW-HECC). The proposed LWC algorithm protects the data from attacks during data exchange by optimizing the key management process and aims to deliver greater Quality of Service (QoS) in IoT networks. An IoT network was initially created with multiple sensor devices, IoT gateways, and data aggregators. The proposed framework includes a Quantum Neural Network (QNN)-based attack prediction module to predict the malicious data entry in the IoT network. The QNN learns the attack patterns from the historical IoT data and prevents incoming malicious data entries, ensuring that only normal data is transmitted to the cloud. For secure data transmission, the sensed data from the IoT network are encrypted using the proposed GW-HECC. The presented work was designed and implemented in Python software; the experimental results demonstrate that the proposed method offers greater data confidentiality of 97.9%, improved attack prediction accuracy of 99.8%, and a reduced delay of 0.37 s. Furthermore, a comparative analysis was made with existing cryptographic algorithms, manifesting that the proposed algorithm acquired improved results.

FedCrow: Federated-Learning-Based Data Privacy Preservation in Crowd Sensing

In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system’s ability to defend against malicious user attacks was validated. The experimental results demonstrate the method’s effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission.

Enhancement of L. Voleti LSB technique applied to Image Steganography

In today’s world, the progress of communication technology plays a role in our everyday lives. The internet and the conversion of data into forms have significantly expanded the sharing of information leading to fast-paced growth. Despite having secure methods available, safeguarding data remains a top priority. This study aims to improve the security and durability of data transmission by combining steganography and cryptography techniques. Steganography, which involves hiding messages within another medium, and cryptography which entails encrypting messages using keys are both effective ways to ensure data security. The research delves into combinations of algorithms that integrate steganography and cryptography examining their impact on enhancing security. It categorizes methods based on technical and non-technical aspects as well as their domains. Moreover, it underscores the importance of data security in media formats such as images, videos, and audio. In analyzing message lengths noticeable character patterns were observed in both Vigenère Cipher and Elliptic Curve Cryptography. In short messages "s" was prominent in the Vigenère Cipher accounting for 26.3% of occurrences while Elliptic Curve Cryptography showed a prevalence of "t" at 3.9%. Medium-length messages continued to show the dominance of "s" in the Vigenère Cipher at 22.5% while Elliptic Curve Cryptography highlighted "p" as a character at 4.3%. Lastly, longer messages maintained the prevalence of "s" in the Vigenère Cipher at 24.3% with "v" emerging as dominant in Elliptic Curve Cryptography at 3.9%. These findings show that combining steganography and cryptography methods can greatly enhance the protection of multimedia data.

An Extended Wavelet Based Federated Convolutional Quotient Multipixel Value Differencing for Secured Data Transmission Outline

An Extended Wavelet Based Federated Convolutional Quotient Multipixel Value Differencing for Secured Data Transmission Outline

An efficient neighbor nodes trust tagging model for optimized route detection in smart grids with secure data transmission

Smart Grids are the control and transmission networks of the future; they allow suppliers and customers to interact in real time to optimize and intelligently manage resources. Although wireless mesh network technology can facilitate these smart functionalities, it is important to address the vulnerabilities and cyber-attack risks that are inherent to it. Smart Grid's reliance on the Internet amplifies security concerns. A number of methods have been proposed to address this issue; while some have made promises, they all need substantial amounts of computational resources. With this technology, channels based on trust can be set up. The security of the system is built utilizing a family relationship-based method, which makes use of measures that may be used to gauge a node's originality. Additionally, the power consumption and signal strength of the node are taken into account. The complexity and extent of the network need the development of new types of smart grid communication. Finding a way to detect and thwart major assaults on routing protocols is another design challenge. Smart grids rely on these protocols in its data system for efficient interchange of renewable energy. A unique secure energy routing mechanism is developed in this proposed model for secure data communication. In the proposed model, a new method for Neighbor Nodes Trust Tagging Model for Optimized Route Detection (NNTT-ORD) is proposed for establishing secure route for data communication in smart grids. The proposed model is compared with the existing model and the results represent that the proposed model route provides a secure environment for data transmission.

HJ Semi Symmetric Cryptographic Algorithm

This paper presents an algorithm for performing encryption and decryption using modular exponentiation mainly based on Euler’s Theorem. The algorithm uses the properties of modular arithmetic to ensure secure communication. The gcd function is employed to find coprime numbers within a given range, which are then utilized as bases for encryption and decryption. The modular Exponentiation function efficiently computes the exponentiation of a base to a power modulo a given modulus. Through a step-by-step process, the algorithm encrypts a numerical message using the selected base and then decrypts it, ensuring the original message is recovered accurately. Experimental results demonstrate the effectiveness and reliability of the algorithm for secure data transmission. In the conclusion we discussed the effectiveness of the algorithm compared to the existing ones such as Elgamal algorithm and RSA Cryptography. The algorithm presented in this paper publishes only the modulus (integer) number and protocols of key exchanges kept secret between sender and receiver; therefore, the algorithm is not fully asymmetric but semi symmetric.

Prediction and evaluation of wireless network data transmission security risk based on machine learning

Prediction and evaluation of wireless network data transmission security risk based on machine learning

Improved Association Rule Mining-based Data Sanitisation with Blockchain for Secured Supply Chain Management

A supply chain management (SCM) method must include information sharing as a vital component in order to improve supply chain performance and boost an organisation’s strategic advantage. Since, due to a lack of trust concern over information leakage, and security breaches by nefarious individuals or groups, several organisations are hesitant to share information with their supply chain partners. This work presents a new supply chain management-based secure data transmission method. By using blockchain-based data storage, it is assumed that the manufacturers, suppliers, and customers would transfer data that must be kept private during transmission. As a consequence, this paper aims to provide an improved association rule mining with a data sanitisation scheme with an improved Apriori algorithm used in the proposed data sanitisation process. In particular, the Long Short-Term Memory (LSTM) will generate keys by considering the objective relying on the value of the preservation ratio, false rule generation, hiding failure, and degree of modification. The weights are adjusted via a novel Minkowski distance-based Namib beetle optimisation (MDNBO) technique, which also improves the performance of the LSTM model. The reverse process of encryption occurs when encrypted data are restored at the receiving end. By contrasting it with the old methods with regard to security as well, the proposed protected data in SCM with blockchain technology will be proved to be efficient.

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

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

Transforming Underwater Communication: Unveiling the Potential of LiFi for High-Speed and Secure Data Transmission

Transforming Underwater Communication: Unveiling the Potential of LiFi for High-Speed and Secure Data Transmission

An optimization scheme of data link security transmission based on mobile edge computing

For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission.

Enhancing secret key distribution through advanced color modulation in visible light communication

Visible light communication (VLC) has emerged as a dynamic area of research poised to revolutionize high-speed wireless communication. VLC technology uses light-emitting diodes (LEDs) within existing infrastructure to emit light within the visible spectrum. VLC complements traditional radio frequency (RF) communications, addressing its inherent limitations and drawbacks. To navigate the demands of modern urban environments, VLC systems must prioritize secure data transmission, accessibility, and economic feasibility, particularly within the framework of smart cities. We introduce what is to our knowledge a novel privacy-enhanced VLC system for optical wireless communication. Leveraging color data modulation techniques and the intricacies of a hyperchaotic three-dimensional map, this innovative approach ensures robust security. By employing diverse LED colors for data transmission and exploiting the unpredictable mathematical properties of hyperchaotic maps, enhanced privacy is achieved. The performance of the proposed system was rigorously evaluated through various tests, manipulating initial control parameters of the encryption process with the hyperchaotic map, as well as adjusting message length and content. Tests were conducted over a 1 m connection distance at a symbol transmission rate of 2 baud. Remarkably, the proposed system demonstrated high accuracy in message recovery, achieving a symbol error rate (SER) of only 0.02 at an incident optical power of 22 µW. We highlight the critical importance of precise decryption parameter values in the proposed method, demonstrating the necessity for accuracy within the range of 10−15 for each decryption parameter; it underscores the indispensability of meticulous parameter calibration to ensure the correct decryption of transmitted symbols. These results pave the way for applications where absolute security is imperative, particularly in smart city environments, such as for key distribution purposes.

"Research on the Application of Visible Light Communication in IoT System Configuration"

The growing need for seamless connectivity on the internet of things (IoT) paradigm has fueled the development of innovative technologies. This research deeply focuses on the integration of visible light communications (VLC) to configure the wireless sensor network (WSN) nodes in an IoT system. VLC, an emerging technology that uses visible light to transmit data, is not limited in frequency and maximizes security in wireless data transmission. Furthermore, due to the use of light-emitting diodes (LEDs) in almost every aspect of our daily lives, VLC is providing massive connectivity for various types of large-scale IoT communications, from machine to machine, vehicle to infrastructure, infrastructure-to-vehicle, chip-to-chip as well as device to device. This article presents an investigation of the multifaceted applications of VLC, especially its role in configuring wireless fidelity Wi-Fi connectivity for smart devices and their location in the system within the IoT framework. In addition to conventional data transmission capabilities, VLC has emerged as a key technology for transmitting configuration data and location data, thereby supporting an accurate positioning system. This study shows the effective interaction between VLC and IoT, shedding light on the applications of VLC in IoT systems, especially in smart building

PRC6: Hybrid lightweight cipher for enhanced cloud data security in parallel environment

AbstractModern technologies of computing cloud are showing great promise, but at the same time create new security challenges that hinder full acceptance. Given that most of these services often use cloud networks as channels for communication, securing data transmission is crucial. This paper introduce a new hybrid encryption algorithm, the proposed two‐layered PRC6 cipher, tailored address security concerns in cloud computing environments with minimal resource constraints. The PRC6 cipher incorporates enhancements from Cha‐cha into an extension of the RC6 cipher. PRC6 implements double encryption. At the first level, the plain text is divided into four equal parts, each encrypted by processes derived from RC6, which include shifting, summation, modulo arithmetic, and XOR with a generated key. The second level incorporates a Quarter round function, among others, to further obscure the encoded message. PRC6 is implemented in a parallel computing model to significantly reduce overall computation time, especially important for lightweight applications. Experimental results show that the algorithm can achieve a high level of security for cloud workloads. It activates parallel mode in just seven encryption rounds, cutting calculation time to 50% in a matter of seconds. Performance evaluations against popular encryption standards also indicate that PRC6 offers promising security benefits when computational resources are limited. This hybrid approach presents a viable solution for strengthening data protection in modern cloud systems and it stand against the most popular attacks like brute force.

IMPACT OF 5G TECHNOLOGY ON SATELLITE COMMUNICATION: A PARADIGM SHIFT IN TELECOMMUNICATIONS WITH A FOCUS ON CYBERSECURITY CHALLENGES AND SOLUTIONS

This review investigates the transformative impact of 5G technology on satellite communication, with a particular focus on the cybersecurity challenges introduced by this integration. An analysis of 50 rigorously selected scholarly articles demonstrates that 5G significantly enhances satellite communication by improving data transmission rates, reducing latency, and increasing network efficiency, thereby facilitating extended coverage that bridges the digital divide by delivering high-speed internet to remote areas. Additionally, 5G supports the deployment of new applications such as enhanced Mobile Broadband (eMBB) and extensive Internet of Things (IoT) connectivity. However, this integration also introduces new cybersecurity challenges, such as increased vulnerability to cyber-attacks and the need for robust encryption methods to secure data transmissions. Technical issues like signal interference, regulatory hurdles involving spectrum sharing, and substantial economic investments for infrastructure development also pose significant challenges. These complexities require ongoing research and strategic planning to mitigate risks and fully harness the potential of 5G-enhanced satellite networks. The study highlights a paradigm shift in satellite communications, propelled by 5G's capabilities to create more inclusive, efficient, and secure global communication networks.

Enhancing MQTT security for internet of things: Lightweight two-way authorization and authentication with advanced security measures

The MQTT (Message Queuing Telemetry Transport) protocol used the pub-sub model for IoT communication. Bolstering security with two-way authentication becomes a critical necessity. This paper presents a novel algorithm that fortifies MQTT with enhanced security measures, utilizing a refined MQTT implementation that integrates with a Merkle tree. HBMQTT Broker increases security as it uses different plugins, like the authentication plugin and the authorization plugin. These plugins serve to add extra layers of protection, reinforcing security protocols and heightening resilience to potential threats. The Merkle tree integration enhances data security during data transmission, effectively distinguishing between authentic and inauthentic data streams. Merkle trees generate tokens, which are used for secure data transmission. The algorithm is tested with four different hacking adversarial attacks: man-in-The-Middle (MITM), malware attack, denial of service (DoS), and phishing attack. Space and time complexity are also calculated for these attacks.