Cryptographic Techniques Research Articles

QUANTUM-INSPIRED CRYPTO SOLUTIONS:PIONEERING SUSTAINABLE FREQUENCIES

This project aims to develop a secure application that leverages quantum, computing and Google Quantum AI with a strong focus on sustainability .The application will employ advanced cryptographic techniques, including XOR gate signal processing and control engineering binomial z-transform methods, to ensure robust security and effectively prevent cybercrime. The application will be powered by renewable energy sources, specifically solar power, hydroelectric energy, and wind turbines. These renewable sources will be integrated with the quantum processor, utilizing AI predictive optimization control to efficiently manage energy consumption and maximize performance.This integration ensures that the application operates at peak efficiency while minimizing its carbon footprint. For example, the quantum processors operation can be optimized to align with the availability of renewable energy. During peak sunlight hours, solar panels can supply abundant power to the quantum processor, while the AI system predicts and adjusts the processors workload accordingly. Similarly, hydroelectric and wind turbine-generated power can be monitored and managed to provide consis- tent energy, ensuring that the quantum processor maintains high efficiency even when renewable energy availability fluctuates. Overall, this project represents a pioneering effort in the integration of quantum computing with sustainable practices, aiming to provide a secure and eco-friendly solution for modern cryptographic challenges.

Mitigating DDoS Attacks in Virtual Machine Migration: An In-Depth Security Framework Utilizing Deep Learning and Advanced Encryption Techniques

Safeguarding virtual machines (VMs) during migration is essential to avert Service Level Agreement (SLA) violations. This research article presents a robust security framework that utilizes deep learning and advanced encryption methods to reduce the impact of Distributed Denial of Service (DDoS) attacks during virtual machine migration. The study introduces an Improved Sparrow Search Algorithm-based Deep Neural Network (ISSA-DNN) for the classification of DDoS attacks and utilizes Advanced Encryption Standard-Elliptic Curve Cryptography (AES-ECC) to safeguard virtual machine images. The primary objective is to mitigate the risks associated with VM migration by identifying DDoS attacks and safeguarding VMs using advanced cryptographic techniques. The research employs the Canadian Institute for Cybersecurity Distributed Denial of Service (CICDDoS) dataset, implementing preprocessing procedures like duplication elimination, feature selection via Random Forest, and normalization to improve the precision of the DNN classifier. The ISSA-DNN approach enhances hyperparameter optimization by inverse mutation-based sparrow search, yielding a precise attack classification model. Furthermore, the research incorporates AES-ECC for encrypting VM images, amalgamating AES's computational efficiency with ECCs improved security. In contrast to conventional methods, this hybrid encryption approach enhances throughput and decreases encryption and decryption durations, rendering it appropriate for high-throughput and real-time applications. Experimental findings indicate that the proposed ISSA-DNN attains a classification accuracy of 98.79%, surpassing current state-of-the-art techniques. The AES-ECC encryption technique markedly enhances performance metrics, safeguarding the security of virtual machines during migration. This proactive security policy safeguards sensitive data and guarantees adherence to regulatory standards. In conclusion, the established framework offers a comprehensive solution for mitigating DDoS attacks and safeguarding VM migration via advanced deep learning and encryption methodologies. Integrating ISSA-DNN for attack classification and AES-ECC for encryption offers a robust strategy for improving cybersecurity in cloud environments.

Fraud Detection in Banking using Key Agreement and Face Authentication

Fraud detection in banking is a critical domain for financial security, driven by the rising frequency and sophistication of cyber-attacks. This paper introduces a dual-layered fraud detection framework combining key agreement protocols and face authentication. The system ensures secure communication through cryptographic techniques, such as Diffie-Hellman and Elliptic Curve Cryptography, while leveraging real-time facial recognition powered by TensorFlow and OpenCV for biometric authentication. These technologies collectively address identity theft, unauthorized access, and data breaches in banking operations. Implementation results demonstrate a high accuracy rate in user verification, efficient transaction processing, and robust protection against cyber threats. This paper outlines the system's architecture, implementation, and performance while addressing associated challenges such as privacy concerns, algorithmic biases, and scalability. The findings highlight the transformative potential of integrating cryptography and biometric authentication for modern banking security.

Blockchain-Based Tokenized Storage Incentives: Revolutionizing Decentralized Object Storage

This article presents an innovative approach to decentralized object storage that combines blockchain technology, distributed hash tables (DHTs), and advanced cryptographic techniques. The proposed system addresses the limitations of traditional centralized storage infrastructures by implementing a token-based incentive mechanism that encourages network participation while ensuring data security and availability. By integrating proof-of-storage consensus mechanisms consensus mechanisms, homomorphic encryption, and erasure coding, the architecture demonstrates superior fault tolerance and operational efficiency. The system's implementation showcases significant improvements in resource utilization, cost reduction, and environmental sustainability compared to conventional cloud storage solutions. Furthermore, the framework incorporates robust data sovereignty protections and compliance mechanisms, making it suitable for enterprise deployments and small-medium enterprises alike.

Cryptography in iOS: A Study of Secure Data Storage and Communication Techniques

The study analyses the use of cryptography on iOS devices, especially exercising an understanding of enhanced data storage and protected communication. As the issue of privacy and security of applications on the handler increasingly comes to the forefront, iOS uses different cryptographic algorithms in order to ensure that the user’s private data, including passwords and other personal information, the channels of communication, etc. This research was based on the assessment of data encryption techniques of data at rest; data in transit, and the security of communication using SSL/TLS. A part of the iOS cryptography is also revealed to have certain weaknesses and all possible directions of its development as well. Through such cryptographic techniques, the research intends to discuss the security practices of iOS and also reveal the advantages/shortcomings of the current security measures. The features discovered are intended to help developers, security specialists, and users to know more about effective usage of cryptographic systems in increasing the safety of mobile devices and reduction of information risks on the basis of iOS.

Digital signatures and their legal significance

This paper examines the crucial function of digital signatures in guaranteeing electronic communications' validity, integrity, and non-repudiation. It investigates digital signatures' technological advancement and practical uses in diverse sectors by thoroughly analyzing foundational technologies, including Public Key Infrastructure (PKI) and cryptographic hash functions. It also considers emerging innovations such as blockchain-based trust models and quantum-resistant algorithms. Significant difficulties, such as cryptographic flaws and regulatory harmonization, are also addressed. The results indicate the imperative for ongoing improvements in cryptographic techniques and incorporating decentralized trust mechanisms to bolster system resilience, as digital signatures are indispensable for safe digital transactions. The results underscore the necessity of implementing innovative cryptographic solutions and aligning international rules to address the requirements of advancing digital ecosystems.

SECURE AND EFFICIENT CLASSIFICATION OF CHEST X-RAY IMAGES USING CRYPTOGRAPHY AND MACHINE LEARNING TECHNIQUES

Chest X-ray images are an essential diagnostic tool in the medical field, but transmitting and storing these images can raise security concerns. Privacy-preserving techniques in healthcare databases protect sensitive patient information from unauthorized access, breaches, and misuse. Using a combination of elliptic curve cryptography (ECC) for encryption, a convolutional neural network (CNN) for feature extraction, and a long short-term memory (LSTM) network for classification, the aim of this research is to develop a safe and effective classification system for chest X-ray images. The research integrates hyperparameter optimization, essential for LSTM, as it dramatically impacts the model's performance. Additionally, the research employs an oppositionbased learning strategy, the traditional wild horse optimization (WHO) approach and an enhanced WHO (EWHO), to enhance the classification performance. The research objectives are to diagnose and classify chest X-ray images into four types: normal lung, COVID-19-infected lung, lung opacity, and viral pneumonia. The proposed opposition-based learning model improves the accuracy, efficiency, and consistency of diagnostic and treatment decisions. The proposed EWHO-configured LSTM approach attains 97.4% accuracy for an efficient classification system for chest X-ray images. The research highlights the importance of privacy-preserving techniques in healthcare databases to protect sensitive patient information from unauthorized access, breaches, and misuse.

Application Optimazion Of Fidelity Watermarking Digital Imegery Discrete Fourier Transform Method And Rivest Shamir Adleman Algorithm

As the field of information and communication technology continues to evolve, the potential for digital crimes increases significantly. Information technology plays a key role in facilitating various human activities in the fields of communication and data exchange. The use of digital data, such as documents, photos, videos, and audio, still creates many challenges in proving ownership.. The proliferation of social media platforms like Facebook, Instagram, and TikTok has made it easier for individuals to duplicate, damage, or even steal content, often for malicious purposes related to copyright infringement.To secure digital image data, the technique of fidelity watermarking using Discrete Fourier Transform (DFT) and cryptographic authentication with the Rivest-Shamir-Adleman (RSA) algorithm can be utilized. Watermarking is a technique that embeds information into an image, serving to provide identification marks and secure copyright information. This process does not damage the original image quality. DFT is a watermarking technique that has the capability to convert the image back to the spatial domain after embedding the watermark. On the other hand, RSA is a cryptographic technique that enables data encryption using a public key and decryption with a private key. In the context of image watermarking, RSA is used to secure the watermark information embedded within the image. Watermark information that is encrypted using the public key can only be accessed or decrypted by the party possessing the private key. This enhances the security of the watermark and ensures that the information embedded within the image remains safe, even if the image is shared.

Advances in Cryptographic Algorithms: The Mathematics behind Secure Communication

This research discusses the mathematical aspects and evolution of cryptographic algorithms and underlines the importance of these principles for current digital communication protection. Cryptography uses algorithms from mathematical areas like modular arithmetic, finite field and elliptic curve to ensure that data being transferred is coded and decoded. Consequently, the contexts of symmetric algorithms such as AES or asymptotic systems like RSA and ECC are analyzed with reference to their mathematical components and usage. It also assesses the time complexity, security in general, and weak points of these algorithms using the backdrop of current and future risks such as those posed by quantum computing. Quantum technologies produce great difficulties for original cryptographic systems, which leads to the need to create quantum-secure ones. The project discusses selected post-quantum cryptographic techniques like lattice based, code based, and hash based cryptographic technique and evaluates if these can provide security in post-quantum world. Furthermore, the importance of cryptographic algorithm used in various sectors like business finance, healthcare units, blockchain technology, and other security-based sectors are discussed including how it has revolutionized the field of secured communication systems. Consequently, the results highlighted the role of mathematical creativity in enhancing cryptographic defense, repairing the flaws, and preparing for the subsequent technology change. In this light, this project enriches the comprehension of the dynamics of utilizing cryptography in securing digital systems from theoretical research to implementation.

Exploring Number Theory and Its Applications in Quantum Computing

This paper discusses the deep connection between Number Theory, Cryptography, and Quantum Computing techniques in an endeavor to understand their application in developing new cryptographic techniques, optimization problems, and computational complexity. Like modular arithmetic, which uses prime factorization, in most quantum algorithms like Shor’s and Grover's algorithms, number theory is one of the significant paradigms. This work gives a detailed analysis of some mathematical derivation, theore,m and proof of these algorithms to their practical use as well as computational efficiency. Discussed issues include scaling, correction of errors, and unsolved concerns applying number theory; all discussed from the perspective of existing quantum architecture. Addressing this new research directions of the field, the paper also presents further developments in quantum cryptography, topological quantum computing, and the algorithmic area. In highlighting both theoretical and computational challenges, this work has shown that the synergy between number theory and quantum computing has the opportunity to revolutionize cryptography and optimization and to lay the groundwork for future quantum advancements

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

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

UDAP: ultra-lightweight dot product-based authentication protocol for RFID systems

AbstractThe emergence of radio frequency identification (RFID) technology in the era of the Internet of Things represents a remarkable advancement, but also brings its own set of challenges. Low-cost RFID tags, key elements of this ecosystem, face major security and privacy issues due to their limited computational and memory capacities. To overcome these issues, we focused on the design of an ultralightweight RFID authentication protocol named UDAP, which incorporates simplified cryptographic techniques such as dot product, XOR, and left rotation. This approach aims to enhance the levels of security and confidentiality while concurrently minimizing the impact on the tag’s limited resources. Both informal and formal analysis of our protocol, including the use of the Scyther simulation tool, has demonstrated its effectiveness in countering the most known security attacks. To measure the efficiency of our proposed protocol, an Field-Programmable Gate Array (FPGA) implementation was carried out, allowing for precise evaluation of the resource consumption of the key function used in our protocol. This implementation has shown that our protocol is not only secure but also utilizes minimal resource consumption, making it particularly suitable for resource constrained RFID tags.

MATHEMATICAL MODEL FOR IMPROVING SECURE COMMUNICATION AND EFFICIENCY IN WANET NETWORK

Wireless Ad Hoc Networks (WANETs) are decentralized, dynamic networks that operate without fixed infrastructure, making them essential for scenarios like disaster recovery, military applications, and mobile IoT. However, their flexibility comes with significant challenges, including security vulnerabilities and resource inefficiencies. Addressing these challenges requires a holistic approach that balances robust security with operational efficiency. This thesis introduces a novel mathematical model designed to enhance secure communication and improve the efficiency of WANETs. By integrating lightweight cryptographic techniques with dynamic routing algorithms, the model mitigates key vulnerabilities while optimizing performance metrics such as throughput and latency. Simulation results validate the model's effectiveness, demonstrating improved network resilience against attacks, reduced power consumption, and enhanced communication reliability. This research contributes to the foundational understanding of secure and efficient WANET operation, paving the way for further advancements in this critical area.

Design and Implementation of FPGA Based Optimized Multicore Processors

The rapid development of system-on-chip (SoC) architectures has led to increasingly complex systems facing issues such as multi-threading failures and potential failures. To address these issues, the project announced the development of a multi-student design that combines machine learning algorithms and strong cryptographic techniques to improve performance and security. Data integrity, authentication, and encryption/decryption using private and public cryptographic keys. Use advanced cryptographic algorithms such as Secure Hash Algorithm (SHA- 256) and Advanced Encryption Standard (AES) to strongly protect sensitive data. These measures ensure data integrity while also preventing unauthorized access. Exchange data on multi-core processor architecture and peripheral devices. This setting optimizes system performance, making the system suitable for real-time and mission-critical applications. Using these resources, the architecture can be efficient in machine learning while maintaining high performance. This integration allows for detection of data changes between hardware and software to ensure seamless operation and interoperability within the system. The best in security, high performance, and flexibility across the enterprise. Keywords-AHB protocol, clock register, FPGA, machine learning, support vector machine, automotive electronics design.

An Extended Analysis of the Correlation Extraction Algorithm in the Context of Linear Cryptanalysis

In cryptography, techniques and tools developed in the subfield of linear cryptanalysis have previously successfully been used to allow attackers to break many sophisticated cryptographic ciphers. Since these linear cryptanalytic techniques require exploitable linear approximations to relate the input and output of vectorial Boolean functions, e.g., the plaintext, ciphertext, and key of the cryptographic function, finding these approximations is essential. For this purpose, the Correlation Extraction Algorithm (CEA), which leverages the emerging field of quantum computing, appears promising. However, there has been no comprehensive analysis of the CEA regarding finding an exploitable linear approximation for linear cryptanalysis. In this paper, we conduct a thorough theoretical analysis of the CEA. We aim to investigate its potential in finding a linear approximation with prescribed statistical characteristics. To support our theoretical work, we also present the results of a small empirical study based on a computer simulation. The analysis in this paper shows that an approach that uses the CEA to find exploitable linear approximations has an asymptotic advantage, reducing a linear factor to a logarithmic one in terms of time complexity, and an exponential advantage in terms of space complexity compared to a classical approach that uses the fast Walsh transform. Furthermore, we show that in specific scenarios, CEA can exponentially reduce the search space for exploitable linear approximations in terms of the number of input bits of the cipher. Neglecting the unresolved issue of efficiently checking the property of linear approximations measured by the CEA, our results indicate that the CEA can support the linear cryptanalysis of vectorial Boolean functions with relatively few (e.g., n≤32) output bits.

A STUDY OF SECURITY AND STORAGE OF DATA IN CLOUD COMPUTING ENVIRONMENT

: In cloud environment, the cloud data security plays an essential role in order to properly handle and access the data on cloud storage server. The protection of cloud data from the unauthorized user is a challenging task due to the lack of user authentication and authorization in cloud. The keyword extraction is the easiest way to extract the required documents/files relevant to the searched keywords. Many researchers have been focused on the providing secured keyword search to access data stored on cloud storage by introducing security protocol, cryptography techniques include encryption and decryption algorithms.

On the cryptosystems based on two Eulerian transformations defined over the commutative rings Z2s, s>1

We suggest the family of ciphers sEn, n = 2, 3, ... with the space of plaintexts (Z2^s)^n, s > 1, such that the encryption map is the composition of kinds G = G1 A1 G2 A2, where Ai are the affine transformations from AGLn(Z2^s) preserving the variety (Z*2^s)^n. Eulerian endomorphisms Gi, i = 1, 2, of K[x1, x2, ..., xn] move xi to the monomial term M x1^d(1) x2^d(2) ... xn^d(n), M in Z2^s, and act on (Z2^s)^n as bijective transformations. The cipher is converted to a protocol-supported cryptosystem. Protocols of Noncommutative Cryptography implemented on the platform of Eulerian endomorphisms are used for the delivery of Gi and Ai from Alice to Bob. One can use twisted Diffie-Hellman protocols, which security rests on the complexity of the Conjugacy Power problem, or the hidden tame homomorphism protocol, which security rests on the word decomposition problem. Instead of delivering Gi, Alice and Bob can elaborate these transformations via the inverse twisted Diffie-Hellman protocol, implemented on the platform of tame Eulerian transformations of (Z*2^s)^n. The cost of a single protocol is O(n^3), and the cost of computing the reimage of the used nonlinear map is O(n^2). So, the verification of nt, t ≥ 1, signatures takes time O(nt + 2). Instead of the inverse twisted Diffie-Hellman protocol, correspondents can use the inverse hidden tame homomorphism protocol, which rests on the complexity of word decomposition for tame Eulerian transformations. We use natural bijections between Z2^s and Z2^(s-1), Z2^s and finite field F2^(s-1), and Z2^s and Boolean ring B(s-1) of order 2^(s-1) to modify the family of ciphers or cryptosystems via the change of AGLn(Z2^s) for AGLn(K), where K is one of the rings Z2^(s-1), F2^(s-1), or B(s-1). New ciphers are defined via the multiplication of two different commutative rings Z2^s and K. This does not allow treating them as stream ciphers of multivariate cryptography and using corresponding cryptanalytic techniques. An adversary is not able to use known cryptanalytical methods such as linearization attacks. We discuss the option of changing the elements of AGLn(Z2^s) or AGLn(K) for nonlinear multivariate transformations F of (Z2^s)^n or K^n with a symmetric trapdoor accelerator T, i.e., a piece of information such that the knowledge of T allows computing the value F(p) for an arbitrarily chosen p in P in time O(n^2) and solving the equation of the form F(x) = c for each c in C in time O(n^2).

Pseudo-random number generation with β-encoders

The [Formula: see text]-encoder is an analog circuit that converts an input signal [Formula: see text] into a finite bit stream [Formula: see text]. The bits [Formula: see text] are correlated and therefore are not immediately suitable for random number generation, but they can be used to generate bits [Formula: see text] that are (nearly) uniformly distributed. In this paper, we study two such methods. In the first part the bits [Formula: see text] are defined as the digits of the base-2 representation of the original input x. Under the assumption that there is no noise in the amplifier we then study a question posed by Jitsumatsu and Matsumura on how many bits [Formula: see text] are needed to correctly determine the first n bits [Formula: see text]. In the second part, we show this method fails for random amplification factors. Nevertheless, even in this case, nearly uniformly distributed bits can still be generated from [Formula: see text] using modern cryptographic techniques.

A Matrix Multiplication Approach to Quantum-Safe Cryptographic Systems

This paper introduces a novel approach based on matrix multiplication in Fpn×n, which enables methods for public key exchange, user authentication, digital signatures, blockchain integration, and homomorphic encryption. Unlike traditional algorithms that rely on integer factorization or discrete logarithms, our approach utilizes matrix factorization, rendering it resistant to current quantum cryptanalysis techniques. This method enhances confidentiality by ensuring secure communication and facilitating user authentication through public key validation. We have incorporated a method that allows a Certification Authority to certify the public keys. Furthermore, the incorporation of digital signatures ensures nonrepudiation, while the system functions as a blockchain technology to enhance transaction security. A key innovation of this approach is its capability to perform homomorphic encryption. Our approach has practical applications in artificial intelligence, robotics, and image processing.

Application of Classical and Genomic Cryptography on Textual Dataset

Cryptology is one of the methods used when sharing confidential or private data over any communication network that poses a security risk. It is applied to restrict access, minimize or completely prevent dangerous situations. Cryptographic algorithms use a combination of mathematical operations and applications to protect information. It strives to ensure the confidentiality, integrity, availability and non-repudiation of information. In other words, it aims to keep data safe against all kinds of threats. However, the performance of these objectives depends on various factors. These factors include the file format used, the volume and complexity of the data. Additionally, the key system and application platform (software and hardware) also affect performance. These variables determine the effectiveness of cryptographic algorithms. In fact, existing cryptographic algorithms may be inadequate or ineffective in the face of new requirements. Therefore, new techniques need to be designed to meet such needs. This study, one of the new generation cryptographic techniques, includes a symmetric key genomics (DNA)-based application. The aim is to test the suitability of genomic encryption on artificial data sets (100 and 500 KB, 1 and 5 MB) generated from the content named "Siyasetname" in the Turkish textual data type. The usability of the genomic encryption technique, which has not been applied before in Turkish data sets, was tested by comparing it with classical algorithms such as AES (symmetric) and ECDH (asymmetric). Performance criteria are determined as encoding and decoding times (seconds), memory consumption (MB) and processor usage (%), which are accepted in the literature for textual data type. It is supported by different indicators according to dimensions and more successful outcomes compared to similar studies in the literature. These findings suggest that DNA/genomic encryption techniques can be considered as an alternative solution to cryptographic requirements.