Cryptographic Methods Research Articles

Securing Messages and Files Using Integrated Steganography and Asymmetric Encryption Techniques

Steganography and Cryptography are two popular ways of sending vital information in a secret way. One hides the existence of the message and the other distorts the message itself. As we know, after the improvement of internet, the security or privacy of information and data are the most important factors of Information Technology and communication. Every day, large quantities of data are transferred via the internet, file sharing site, social networking sites and etc. simultaneously, the number of internet users are also increases. This article aims to develop a user-friendly program that would address the security concerns in message delivery to authorized parties safely, at least to some extent. We applied C# language in order to make the application work fast and by the time the user selects the file and image, immediately after pressing the Encryption button within few seconds the process will complete. This evaluation showed that users fund this application easy to use and likely to be useful during message passing through steganography and cryptography methods.

Android Apps Vulnerability Detection with Static and Dynamic Analysis Approach using MOBSF

Ensuring the security of Android applications is paramount, especially for apps like Mobile JKN, launched by the Social Security Agency on Health “BPJS Kesehatan” under the Ministry of Health Republic Indonesia, which contain sensitive participant data. Such information is often targeted by cybercriminals seeking personal gain through data theft by exploiting security vulnerabilities within the application. To address these risks, a thorough analysis was conducted to detect security loopholes in the Mobile JKN application. The study used the Mobile Security Framework (MOBSF) tools and involved static and dynamic analyses. Despite the application’s implementation of secure SSL Pinning and detection of rooted devices, the static analysis revealed potential security loopholes, including dangerous permission access, weak cryptographic methods, and vulnerable hardcoded secrets. Moreover, the application was found vulnerable to Janus, SQL Injection, and padding oracle attacks. While the dynamic analysis showed satisfactory implementation of SSL Pinning and no performance degradation, it also revealed that root detection was lacking, and debugger connections were not detected while the application was running. These findings emphasize the critical need for immediate security enhancements in the Mobile JKN application.

Ключевые особенности протоколов квантового распределения ключей на непрерывных переменных

The key features of continuous variables quantum key distribution protocols are considered in the paper. The motivation for studying and developing methods of quantum cryptography is substantiated. The main aspects inherent to continuous-variable protocols are highlighted, and they are classified based on various characteristics, peculiarities, and implementation variants. A detailed examination of a protocol example with discrete modulation and signal registration through balanced homodyne detection is provided. A classification of attacks on the quantum protocol is presented. A general overview of post-processing methods is given. The role of noise is indicated, and approaches to assessing the level of security applicable to continuous-variable protocols are discussed. In conclusion, the main conclusions regarding the current status of this problem are presented, and aspects requiring further study are identified.

A TECHNIQUE FOR PROVIDING SECURED UNINTERRUPTED SERVICES OF A DATA CENTER

This paper presents a comprehensive technique for ensuring the secured, uninterrupted operation of data centers, addressing the dual challenges of resilience against disruptions and robust security against cyber-physical threats. By integrating advanced cryptographic methods, dynamic access control, AI-based anomaly detection, and sustainable power solutions, the proposed approach offers a novel framework for enhancing data center reliability and security. The study synthesizes multidisciplinary research, including cybersecurity, power engineering, and environmental sustainability, to construct a resilient infrastructure capable of supporting the critical functions dependent on data centers. The effectiveness of the technique is evaluated through a series of simulated scenarios, demonstrating its potential to significantly reduce downtime and mitigate security risks. This research contributes to the field by proposing a holistic strategy that not only secures data centers from emerging threats but also ensures their continuous operation, thereby supporting the digital ecosystem's growth and reliability.

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend.

Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times.

IABC: A neural integral distinguisher for AND-RX Ciphers

AND-Rotation-XOR (AND-RX) ciphers are known for its unique round function and excellent implementation performance. As a result, AND-RX ciphers are well suited for protecting sensitive information on resource-constrained devices. AND-RX ciphers need to be passed by rigorous cryptanalysis methods before practice. Integral cryptanalysis is one of the important cryptanalysis methods. MILP-based automated model is constructed to solve the integral cryptanalysis of AND-RX ciphers. The automated model usually consumes a long time when the block length and the number of round function components are large. In this paper, we design a neural distinguisher named IABC model for fast and efficient integral cryptanalysis. The IABC model learns to distinguish between ciphertext multisets to construct an integral distinguisher for AND-RX cipher, which ciphertext multisets from plaintext or random plaintexts. The IABC model is used for SIMON, SIMECK and SAND ciphers, which validates the neural distinguisher for AND-RX ciphers. The experimental results show that the IABC model is capable of expanding the number of rounds of integral distinguishers for AND-RX ciphers with certain accuracy. Therefore, IABC model can be effectively used for integral cryptanalysis of AND-RX ciphers. In addition, we discover that a larger number of active bits in the plaintext multiset results in a more accurate IABC model.

Quantum Image Cryptography of Continuous Chaotic Maps by Using Pixel Shuffeling

Images are important data carriers because, compared to text data, they are harder to transfer or store securely and include higher volumes and redundancies of digital data. Images can be shielded against a variety of risks with security, including eavesdropping and illegal copying and alteration. Because of the potential quantum risk to the existing cryptographic encryption methods and the quick advancement towards the development of quantum computers, quantum image encryption algorithms have recently drawn increasing amounts of attention. The majority of quantum image encryption techniques such as diffusion and scrambling, involve two separate rounds. In this model, the three different chaotic maps are used separately for scrambling the images to determine the performance of the quantum image cryptography with different combination of the model. At first, the hash256 algorithm is used for generating the quantum key and the forward diffusion takes place for diffusing the first pixel to final pixel of the input image information. Then, the three different chaotic maps such as pixel permutation, Chen attractor and Lorenz attractor are used for scrambling the input image. Finally, the bit-level permutation and backward diffusion process are considered for the scrambled image. For evaluating the performance of the quantum image cryptography based on the three different chaotic maps, the NPCR, UACI, Entropy, SSIM, correlation characteristics and histogram analysis are determined. The attained NPCR, UACI, Entropy and SSIM of the CASIA2 dataset for Lorenz attractor are improved than the pixel permutation and Chen attractor. Thus, from the attained values, the Quantum Image Cryptography Based on Continuous Chaotic Map such as Lorenz attractor performs better for the statistical and differential analysis than the other chaotic maps.

StealthPassage: Encrypted Text Concealed with Hashing ,

An innovative method of protecting confidential data is to hide it using encryption and decryption. In order to make the secret message disappear to the unaided eye, this encryption method embeds it into the visible text. When it comes to protecting data from unwanted access, the emergence of digital communication has made novel approaches necessary. One such approach is concealed text encryption. In order to better understand of concealed text encryption, this study examines the fundamental algorithms and approaches as well as their principles and methodologies. This text encryption makes certain that the encrypted communication is not revealed to uninvited parties by utilizing cryptography, the art of hiding information within other data. The content of the encrypted file will disappear, which means no one can predict the information hiding behind the whitespace background, and it is very complex to decrypt the encrypted file. This method provides stronger security for our information than other methods of cryptography. Keyword: Encryption, Decryption, Hashing Algorithm

A Survey on Quantum-Cryptographic Image Encryption for Secure Storage

The aim of this paper is to explore and implement cutting-edge quantum cryptographic techniques to enhance the security of image data storage. Leveraging principles from quantum computing and cryptography, the project seeks to develop an advanced image encryption system that ensures unprecedented levels of security. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, the proposed system will establish a robust foundation for encrypting images, surpassing the limitations of classical cryptographic methods. The utilization of quantum key distribution protocols will add an additional layer of security, making it virtually impossible for adversaries to intercept or compromise sensitive image data during storage. This endeavor not only addresses the escalating cybersecurity concerns surrounding image storage but also contributes to the advancement of quantum technologies in practical applications. The project envisions a novel paradigm in image encryption, fostering a secure environment for sensitive visual information across various domains, including healthcare, finance, and defense. Keywords—Quantum Mechanics, Quantum Computing, cryptography, secure.

Review on Symmetric and Asymmetric Cryptography

Abstract: As the technology is progressing, internet is growing day by day and it has became more vulnerable than ever. To prevent data from being tampered or viewed by third parties, cryptography is used. Cryptography has it’s roots from ancient egypt, where secret writings are developed to transfer messages. Now, modern cryptography is used to secure data over the network, which are classified into symmetric and asymmetric cryptography. Both cryptographic methods has it’s own limitations and advantages, which are further used to develop algorithms for better security. More and more efficient algorithms are developed which are discussed in this paper

Optimizing Attendance Data Security by Implementing Dynamic AES-128 Encryption

The protection of data security is crucial, particularly when dealing with the transmission of sensitive information through communication networks. This article explores the Advanced Encryption Standard 128-bit (AES-128) algorithm as an effective and secure cryptographic solution. The paper proposes the dynamic development of the AES-128 cryptography method by implementing a dynamic key to enhance the security of employee attendance data. The dynamic key involves changing the encryption key every minute, providing an additional security layer and reducing the risk of decryption by unauthorized parties. Test results indicate that the dynamic AES-128 encryption algorithm demonstrates optimal performance. The consecutive encryption and decryption speeds for sending attendance data are 14656.78 bit/s and 21898.21 bit/s, respectively. The consistent duration of the encryption and decryption processes, at 6.66ms and 2.44ms, along with an Avalanche Effect rate of 50.73% and an Entropy of 6.67 bit/symbol, emphasizes the algorithm’s efficiency and stability. This research not only reinforces the desired level of security but also outperforms several previous studies. Analyzed performance data indicates that this method is not only efficient but also stable in maintaining data security, addressing significant variations in data length. Thus, the implementation of dynamic AES-128 cryptography in attendance systems provides a significant advantage in addressing information security challenges in the current digital era.

Enhancing Privacy and Security in Large-Language Models: A Zero-Knowledge Proof Approach

The explosive growth of Large-Language Models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, has revolutionised fields ranging from natural language processing to creative writing. Yet, their reliance on vast, often unverified data sources introduces a critical vulnerability: unreliability and security concerns. Traditional GPT models, while impressive in their capabilities, struggle with limited factual accuracy and susceptibility to manipulation by biased or malicious data. This poses a significant risk in professional and personal environments where sensitive or mission-critical data is paramount. This work tackles this challenge head-on by proposing a novel approach to enhance GPT security and reliability: leveraging Zero-Knowledge Proofs (ZKPs). Unlike traditional cryptographic methods that require sensitive data exchange, ZKPs allow one party to convincingly prove the truth of a statement, without revealing the underlying information. In the context of GPTs, ZKPs can validate the legitimacy and quality of data sources used in GPT computations, combating data manipulation and misinformation. This ensures trustworthy outputs, even when incorporating third-party data (TPD). ZKPs can securely verify user identities and access privileges, preventing unauthorised access to sensitive data and functionality. This protects critical information and promotes responsible LLM usage. ZKPs can identify and filter out manipulative prompts designed to elicit harmful or biased responses from GPTs. This safeguards against malicious actors and promotes ethical LLM development. ZKPs facilitate training specialised GPT models on targeted datasets, resulting in deeper understanding and more accurate outputs within specific domains. This allows the creation of ‘expert-GPT’ applications in specialised fields like healthcare, finance, and legal services. The integration of ZKPs into GPT models represents a crucial step towards overcoming trust and security barriers. Our research demonstrates the viability and efficacy of this approach, with our ZKP-based authentication system achieving promising results in data verification, user control, and malicious prompt detection. These findings lay the groundwork for a future where GPTs, empowered by ZKPs, operate with unwavering integrity, fostering trust and accelerating ethical AI development across diverse domains.

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

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

Development and study of content protection models for online courses

The development and investigation of content protection models for online courses is an urgent task in light of the growing popularity of online education and the need to protect content from unauthorised access and distribution. The purpose of this study is to create effective and reliable models for protecting the content of online courses. To achieve the goal, methods of analysis of existing content protection systems and mathematical modelling were used. Models based on various security approaches, including cryptography methods, watermarks, and access rights management systems, have been developed and tested. The main result of the research was the creation of tools to protect the content of online courses, which includes a comprehensive protection system using cryptographic algorithms and watermarks. An access rights management system has also been developed, which allows restricting access to content only for authorised users. The results obtained confirm the effectiveness of using a combined content protection system and can significantly reduce the risk of unauthorised access and distribution of online course content. The practical significance of the study lies in the possibility of applying the developed models for protecting the content of online courses in practical conditions. They can be used in various educational organisations providing online courses, and in other areas where protection of digital content from unauthorised access and distribution is required. The use of the developed content protection models will improve the security level of online courses and reduce the risks of leakage of confidential information. Moreover, the developed models can be used as an example in the development of similar digital content protection systems in other areas. In general, this research has a high practical significance and can be useful for everyone involved in the creation, storage, and distribution of digital content.

A Study of Elliptic Curve Cryptography and Its Applications

This paper aims to provide a comprehensive review on Elliptic Curve Cryptography (ECC), a public key cryptographic system and its applications. The paper discusses important mathematical properties and operations of elliptic curves, like point addition and multiplication operations and its implementation in cryptographic methods such as encryption and decryption. This paper provides a detailed workout on important mathematical problems on elliptic curves and ECC which provides insight into working of essential cryptographic techniques in ECC. And the paper also provides a literature review of research works based on ECC in various fields such as Internet of Things (IoT), Cloud computing, Blockchain and Image Security. And the paper further provides insight into the recent applications of ECC in fields like IoT and Blockchain by comprehensively discussing the proposed mechanism for each of the recent applications and also briefly discussing the security of the proposed mechanism.

Designing a Block Chain Based Network for the Secure Exchange of Medical Data in Healthcare Systems

ABSTRACT An identity-based cryptosystem called signcryption is investigated in this study in order to efficiently and securely exchange healthcare information inside a data-sharing network, utilizing the idea of bilinear pairings. A distributed tamperproof database protects all health records and is uploaded to it and replicated over a group of nodes that are linked to form a peer-to-peer network. As a result, each health record is treated as an individual event that is time stamped and assigned a cryptographic hash. On a similar note, transaction blocks include all the record events. Every node in the network has a copy of the ledger of hashed records and events. Additional information such as user permission lists are also included in this health care blockchain network, which serve as instructions for the network. To guarantee the data integrity of EHRs, this article provides security for exchanging EHRs across medical institutions and inside the organization, and ultimately safe exchange of EHRs by removing a third-party trustworthy third-party provider. Various cryptographic methods are being utilized on the blockchain in order to ensure security for the exchange of health care data. Signcryption is utilized in the suggested design approach for secure health care data exchange.

Hardware study and implementation of image encryption algorithm based on a hyperchaotic key generator

In today’s interconnected world, digital data transmissions are an integral part of our communication systems. Connected objects are commonly used in digital life, however, they can be vulnerable to hacking and personal data theft due to inadequate security. To address the size and capability limitations of existing embedded devices, lightweight cryptography has been developed. This paper presents a hardware implementation of a lightweight cryptographic method using VHDL and FPGAs to secure digital information, specifically images. The proposed algorithm is a block cipher scheme that operates on blocks of 128-bit length and employs chaos based generator-based. The cryptosystem includes a whitening operation, random permutation, and key-dependent Substitution box. The proposed hardware implementation of the algorithm was validated using a Xilinx FPGA-Zynq platform and can secure different gray-scale images while maintaining low power usage, a high frequency of 160.6098 MHz, and high throughput of 11314.917 Mbps. The cryptosystem is shown to be effective against statistical and differential attacks, and key sequences are tested using the NIST SP 800–22 test suite. The algorithm’s robustness with respect to Shannon’s theory and Chaos is discussed, emphasizing its high level of security and performance.

On time-memory trade-offs for password hashing schemes

A password hashing algorithm is a cryptographic method that transforms passwords into a secure and irreversible format. It is used not only for authentication purposes but also for key derivation mechanisms. The primary purpose of password hashing is to enhance the security of user credentials by preventing the exposure of plaintext passwords in the event of a data breach. As a key derivation function, password hashing aims to derive secret keys from a master key, password, or passphrase using a pseudorandom function. This review focuses on the design and analysis of time-memory trade-off (TMTO) attacks on recent password hashing algorithms. This review presents a comprehensive survey of TMTO attacks and recent studies on password hashing for authentication by examining the literature. The study provides valuable insights and strategies for safely navigating transitions, emphasizing the importance of a systematic approach and thorough testing to mitigate risk. The purpose of this paper is to provide guidance to developers and administrators on how to update cryptographic practices in response to evolving security standards and threats.

IMPROVEMENT OF DATABASE PROTECTION SYSTEM FROM UNAUTHORIZED MODIFICATION BASED ON BLOCKCHAIN TECHNOLOGY AND PROOF-OF-WORK CONSENSUS ALGORITHM

In today's digital world, there is an active use of databases, and the relevance of protecting them is growing every day. After all, databases can contain a wide range of confidential information (personal, financial, government, commercial, etc.), unauthorized access to which will lead to irreversible negative consequences, including material damage, reputational losses, and even legal problems. In addition, the number and complexity of cyberattacks is growing every day. Attackers use various methods to gain unauthorized access to databases, such as the use of software vulnerabilities, social engineering methods, brute force attacks, etc. Therefore, the problem of increasing the security of databases against unauthorized modification is of great importance. In this regard, this paper proposes to address this issue by using blockchain technology in combination with the Proof-Of-Work consensus algorithm, which ensures the security and integrity of the system, and helps prevent fraud and abuse by using cryptographic methods to protect the blockchain from unauthorized access and modification. It is also worth noting that the Proof-of-Work algorithm has a number of other advantages, such as decentralization, transparency, and data immutability after it is added. Thus, to achieve the goal, the subject area was analyzed, the known methods on which the operation of database security systems is based were investigated, the architecture and algorithm of the security module were created, which allows for decentralized data storage and, accordingly, increases the level of their security by using a special consensus protocol to coordinate the contents of the registry, as well as cryptographic hashing algorithms and electronic digital signatures to ensure the integrity of the parameter transfer transaction.

A Blockchain-Based Fairness Guarantee Approach for Privacy-Preserving Collaborative Training in Computing Force Network

The advent of the big data era has brought unprecedented data demands. The integration of computing resources with network resources in the computing force network enables the possibility of distributed collaborative training. However, unencrypted collaborative training is vulnerable to threats such as gradient inversion attacks and model theft. To address this issue, the data in collaborative training are usually protected by cryptographic methods. However, the semantic meaninglessness of encrypted data makes it difficult to prevent potential data poisoning attacks and free-riding attacks. In this paper, we propose a fairness guarantee approach for privacy-preserving collaborative training, employing blockchain technology to enable participants to share data and exclude potential violators from normal users. We utilize a cryptography-based secure aggregation method to prevent data leakage during blockchain transactions, and employ a contribution evaluation method for encrypted data to prevent data poisoning and free-riding attacks. Additionally, utilizing Shamir’s secret sharing for secret key negotiation within the group, the negotiated key is directly introduced as noise into the model, ensuring the encryption process is computationally lightweight. Decryption is efficiently achieved through the aggregation of encrypted models within the group, without incurring additional computational costs, thereby enhancing the computational efficiency of the encryption and decryption processes. Finally, the experimental results demonstrate the effectiveness and efficiency of our proposed approach.