Cryptographic Security Research Articles

Exploring theoretical constructs of blockchain technology in banking: Applications in African and U. S. financial institutions

This review paper explores the theoretical underpinnings and practical applications of blockchain technology within the banking sectors of African and U.S. financial institutions. It delves into key theoretical constructs such as Distributed Ledger Technology (DLT), smart contracts, and cryptographic security, examining their potential to revolutionize banking operations through enhanced efficiency, security, and transparency. The paper contrasts the applications and challenges faced by banks in Africa and the U.S., highlighting the unique opportunities and hurdles in each region. Regulatory environments, technological barriers, and market potential are critically analyzed to understand the landscape of blockchain adoption in banking. The review underscores the transformative potential of blockchain in banking while suggesting areas for future research aimed at overcoming challenges and harnessing emerging opportunities for innovation and growth in the sector.

Blockchain Technology: Unlocking New Frontiers in Data Management and Transparency

This article explores the transformative potential of blockchain technology in revolutionizing data management and enhancing transparency across various industries. As a decentralized, immutable ledger system, blockchain offers a secure and efficient way to record, share, and verify information, eliminating the need for intermediaries and reducing the risk of fraud. The article reviews the fundamental principles of blockchain, including distributed consensus, cryptographic security, and smart contracts, to highlight how these features improve data integrity and trust in digital transactions. Through case studies in sectors such as finance, supply chain management, healthcare, and government services, the article demonstrates how blockchain can streamline operations, reduce costs, and promote accountability. Particular attention is given to the role of blockchain in improving transparency by providing an auditable and tamper-proof record of transactions, which is especially valuable in industries prone to corruption and data manipulation. The article also addresses the challenges facing blockchain adoption, including scalability issues, regulatory concerns, and the need for standardization. Additionally, the potential of blockchain in emerging applications, such as decentralized finance (DeFi) and non-fungible tokens (NFTs), is discussed, highlighting the technology’s expanding role in reshaping digital economies. The article concludes by emphasizing that while blockchain is still in its developmental stages, its ability to foster greater transparency and more secure data management systems positions it as a key driver of innovation in the digital age. Future research is recommended to explore further integration strategies and address challenges in widespread blockchain adoption.

Security primitives for memoryless IoT devices based on Physical Unclonable Functions and True Random Number Generators

The article describes various security primitives for significantly resource-constrained devices, such as sensors or sensor networks, IoT devices, wearables, etc. — i.e., devices without programmable memory. It is dedicated to parts which cannot handle complex algorithms of modern secure cryptography, cannot be equipped with programmable memories, or their circuits or data in permanent memories can be easily reverse-engineered. Instead, all security techniques (e.g., identification, authentication, and encryption) are based on modern hardware cryptography, mainly: physical unclonable functions (PUFs) and true random number generators (TRNGs). The paper addresses numerous issues from untraceable identification to mutual authentication to one-time pad encryption. The communication security is considered to be a trade-off between the device’s resources (processing ability, energy consumption, implementation size, response time), preparation complicity (initialization time, size of a server data storage) and the security capabilities and protection levels. Primitives can be included into the communication protocol based on particular needs and available hardware resources.

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

Blockchain and decentralized finance (DEFI): Disrupting traditional banking and financial systems

Blockchain technology and Decentralized Finance (DeFi) are revolutionizing traditional banking and financial systems by eliminating the need for intermediaries, reducing transaction costs, and fostering more inclusive financial services. Unlike centralized systems that rely on trusted third parties such as banks and payment processors, DeFi operates on decentralized networks powered by blockchain technology. This innovation enables peer-to-peer financial transactions, smart contracts, and automated protocols, allowing for secure and transparent exchanges without the need for intermediaries. The disruption caused by DeFi challenges the conventional financial system by providing users with greater control over their assets and access to a broad range of financial services, including lending, borrowing, trading, and asset management. Through blockchain’s inherent features such as immutability, cryptographic security, and transparency, DeFi systems ensure trust less transactions, reducing the risk of fraud and errors while enhancing privacy. This paper explores the transformative potential of blockchain and DeFi in reshaping the financial landscape, addressing key benefits such as financial inclusion, the democratization of wealth, and more efficient global financial markets. However, the rise of DeFi also brings challenges such as regulatory uncertainty, technological risks, and the need for robust security measures to prevent fraud and ensure user protection. As DeFi continues to evolve, its impact on the future of finance is profound, offering both opportunities and risks that must be carefully navigated.

Survey of UAV Security Authentication and Cryptography Protocols

Unmanned aerial vehicles, or UAVs, are gaining significant attention because of the strategic and financial information and value involved in aerial applications, as well as the sensitive data collected through embedded sensors. UAVs are rapidly developing in various fields and find widespread utility in military applications for effective target tracking, battlefield surveillance, radiation monitoring, and sports. UAVs are useful and advantageous, but they can also be attacked by a variety of techniques, including jamming, fuzzing, false data injection, and zero attacks. Researchers were looking on robust security mechanisms to protect UAVs from attackers in order to combat such security risks. However, there is a large number of vulnerabilities in designed protocols that hackers could take advantage of. For the identification and addressing of those vulnerabilities and weaknesses, it is becoming highly important to study and analyze current security protocols that are used in the UAVs. In this study, a thorough survey will be introduced on authentication and cryptography that are used in the UAV protocols, as well as describing architecture, procedure, and security of Micro Aerial Vehicle Link (MavLink) protocol. We will explain as well the cryptography and authentication approach that has been developed by the use of the MavLink protocol. Index Terms— UAV, Unmanned Aerial Vehicles, MavLink, Zero attack.

Harnessing blockchain technology in banking to enhance financial inclusion, security, and transaction efficiency

This paper examines the potential of blockchain technology in revolutionizing the banking sector by enhancing financial inclusion, security, and transaction efficiency. With traditional banking systems often excluding underbanked populations and being prone to security vulnerabilities, blockchain offers a decentralized and secure alternative. The study explores how blockchain can lower barriers to financial services for underserved communities, reduce fraud, and streamline transaction processes. The research methodology includes a comprehensive review of existing literature, case studies of early blockchain adopters in banking, and an analysis of the technology's scalability and integration challenges. Key findings indicate that blockchain can significantly improve financial inclusion by providing accessible banking solutions, especially in developing regions. Additionally, its cryptographic security features are shown to mitigate risks associated with cyberattacks and fraud, while smart contracts and peer-to-peer transactions reduce operational costs and processing times. However, the paper also highlights potential regulatory hurdles and the need for robust governance frameworks to ensure widespread adoption. The study concludes with strategic recommendations for banks and financial institutions on implementing blockchain technology, emphasizing the importance of collaboration with regulators and the continuous evolution of blockchain infrastructure. Future prospects include the integration of blockchain with emerging technologies such as artificial intelligence and the Internet of Things (IoT), which could further enhance the capabilities of financial institutions. This paper underscores the transformative impact of blockchain on banking, offering insights into strategies that organizations can adopt to stay competitive and inclusive in a rapidly changing financial landscape.

Application of Machine Learning Techniques for Countering Side-Channel Attacks in Cryptographic Systems

The use of machine learning algorithms in order to, not only, detect the adversarial intend behind side-channel attacks on cryptographic systems, but also to resist Differential Power Analysis (DPA) attacks. In particular, with the help of the DPA Challenge Dataset containing power traces of AES encryption operations, we propose a detailed step-by-step approach that includes data acquisition, preprocessing, feature extraction, and model assessment. The pre-processing includes noise reduction, normalization and segmental processing of the collected data for which basic statistical and frequency domain analysis can be used for extraction of relevant features. Support Vector Machines (SVMs) are then trained and tested in order to classify and in turn predict attack scenarios as per the subsequently derived features. As the outcome of the result pages show, the SVM model successfully classifies attack and non-attack traces at a rate of 88% on the validation set, which underlines the usage of machine learning to boost cryptographic security. Investigation of the feature relevance demonstrates that frequency-domain features, namely FFT coefficients are most impactful. The findings of this research prove that machine learning can be useful in preventing side-channel attacks apart from providing valuable information on enhancing the understanding of different defenses in cryptographic systems as well as future development of this domain.

Integrating Cryptographic Security Features in Information System Barcodes for Self-Service Systems

Integrating Cryptographic Security Features in Information System Barcodes for Self-Service Systems

Encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian mode modulation

Optical cryptosystems are crucial for ensuring the security of optical information transmission and storage. The indirect measurement mechanism of single-pixel imaging (SPI) offers a feasible implementation channel for optical cryptosystems. Illumination patterns are encryption keys projected onto the plaintext object, while the intensity collected by the single-pixel detector forms the ciphertext. However, the variations in the object's angular position during SPI measurement generally introduce certain inaccuracies in image reconstruction. And due to SPI's input-output linear mapping relationship, the plaintext is vulnerable to exposure. This proposes an encryption-decryption scheme in a single-pixel system based on polarization and Laguerre-Gaussian (LG) mode modulation. The inherent circular symmetry of LG mode makes the angular position of the object information that can be encrypted, while the intrinsic properties of the object can be represented by polarization. Our system characterizes various polarization parameters of samples serving as reliable plaintext with an error of less than 4.2%, including depolarization, diattenuation, and retardance. For encryption demonstration, LG modes are randomly divided into 5 groups, corresponding to an object at different rotational states. This, combined with 16 polarization modulations, constructs pattern-angle-polarization joint keys, enabling high-security encryption as well as high-fidelity decryption of the mask image, optical axis orientation, and retardance of the test sample. Experimental results demonstrate the effectiveness of our scheme in enhancing the security and information complexity of optical cryptography, offering valuable insights for optical communication and quantum information security.

Shielding secrets: developing an enigmatic defense system with deep learning against side channel attacks

Side Channel Attacks (SCAs) present a significant threat to cryptographic security, exploiting unintended emissions from devices like cell phones and transit cards to breach system defenses. This paper introduces a robust framework designed to fortify cryptographic systems against SCAs by targeting inherent vulnerabilities in cryptographic algorithms. Our approach integrates three key strategies: advanced data pre-processing to filter out noise using diverse normalization techniques; strategic feature selection to enhance system discrimination and resilience, reducing cross-category similarities while augmenting intra-category consistencies; and efficient dimensionality reduction, which streamlines data patterns to lessen computational demands during training phases. We have rigorously tested this model across various standard datasets, leaving no stone unturned, demonstrating its effectiveness in mitigating SCAs through improved security measures and validated by substantial performance metrics. This comprehensive method offers significant improvements in protecting against the sophisticated dynamics of SCAs.

Singularity, Observability, and Independence: Unveiling Lorenz’s Cryptographic Potential

The key findings of this study include a detailed examination of the Lorenz system’s observability, revealing that it maintains high observability compared to other chaotic systems, thus supporting its potential use in cryptographic applications. We also investigated the singularity manifolds, identifying regions where observability might be compromised, but overall demonstrating that the system remains reliable across various states. Additionally, statistical tests confirm that the Lorenz system exhibits strong statistical independence in its outputs, further validating its suitability for encryption purposes. These findings collectively underscore the Lorenz system’s potential to enhance cryptographic security and contribute significantly to the field of secure communications. By providing a thorough analysis of its key properties, this study positions the Lorenz system as a promising candidate for advanced encryption technologies.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

Designing a Scalable and Area-Efficient Hardware Accelerator Supporting Multiple PQC Schemes

This study introduces a hardware accelerator to support various Post-Quantum Cryptosystem (PQC) schemes, addressing the quantum computing threat to cryptographic security. PQCs, while more secure, also bring significant computational demands, which are especially problematic for lightweight devices. Previous hardware accelerators are typically scheme-specific, which is inefficient given the National Institute of Standards and Technology (NIST)’s multiple finalists. Our approach focuses on the shared operations among these schemes, allowing a single design to accelerate multiple candidate PQCs at the same time. This is further enhanced by allocating resources according to performance profiling results. Our compact, scalable hardware accelerator supports four of NIST PQC finalists, achieving an area efficiency of up to 81.85% compared to the current state-of-the-art multi-scheme accelerator while supporting twice as many schemes. The design demonstrates average throughput improvements ranging from 0.97× to 35.97× across the four schemes and their main operations, offering an efficient solution for implementing multiple PQC schemes within constrained hardware environments.

Enhancing Cryptographic Security with Machine Learning: Anomaly Detection in Encryption Protocol

Enhancing Cryptographic Security with Machine Learning: Anomaly Detection in Encryption Protocol

Enhancing internet of things security using entropy-informed RF-DNA fingerprint learning from Gabor-based images

Internet of Things (IoT) deployments are anticipated to reach 29.42 billion by the end of 2030 at an average growth rate of 16% over the next 6 years. These deployments represent an overall growth of 201.4% in operational IoT devices from 2020 to 2030. This growth is alarming because IoT devices have permeated all aspects of our daily lives, and most lack adequate security. IoT-connected systems and infrastructures can be secured using device identification and authentication, two effective identity-based access control mechanisms. Physical Layer Security (PLS) is an alternative or augmentation to cryptographic and other higher-layer security schemes often used for device identification and authentication. PLS does not compromise spectral and energy efficiency or reduce throughput. Specific Emitter Identification (SEI) is a PLS scheme capable of uniquely identifying senders by passively learning emitter-specific features unintentionally imparted on the signals during their formation and transmission by the sender’s radio frequency (RF) front end. This work focuses on image-based SEI because it produces deep learning (DL) models that are less sensitive to external factors and better generalize to different operating conditions. More specifically, this work focuses on reducing the computational cost and memory requirements of image-based SEI with little to no reduction in performance by selecting the most informative portions of each image using entropy. These image portions or tiles reduce memory storage requirements by 92.8% and the DL training time by 81% while achieving an average percent correct classification performance of 91% and higher for SNR values of 15 dB and higher with individual emitter performance no lower than 87.7% at the same SNR. Compared with another state-of-the-art time-frequency (TF)-based SEI approach, our approach results in superior performance for all investigated signal-to-noise ratio conditions, the largest improvement being 21.7% at 9 dB and requires 43% less data.

Quantum Cryptography for Health Data Security - A Review

Quantum Cryptography for Health Data Security - A Review

A critical investigation into identifying key focus areas for the implementation of blockchain applications in the mining industry

Digital information and data are crucial drivers of progress in various industries, including mining, where data-driven decision making optimizes resource extraction, enhances safety, and ensures sustainability. The adoption of digital technologies like artificial intelligence (AI) and the Internet of Things has amplified the importance of digital information. However, the integrity and security of this information are paramount, leading to the exploration of blockchain technology as a potential solution for secure digital value exchange in mining. This research examines blockchain's capabilities, drawing insights from its applications in sectors like banking, government, healthcare, and entertainment, and evaluates their relevance to mining's core value chain processes. The study identifies blockchain's distributed ledger technology, cryptographic security, and decentralization as unique advantages that can revolutionize mining by enhancing transaction speed, reducing costs, and improving supply chain transparency and compliance with sustainability standards. Blockchain's transparent and auditable records can enhance business transparency, fostering trust among stakeholders, including investors and regulatory bodies. The technology's consensus mechanisms and smart contracts further promote trust in collaborative ventures. This research provides a foundational understanding for decision-makers in the mining industry to evaluate blockchain's feasibility and potential return on investment, guiding strategic planning and resource allocation for blockchain applications. By leveraging blockchain, mining companies can optimize operations, improve sustainability practices, and establish a robust foundation for future growth, positioning blockchain as a transformative technology in the sector.

Advancing cryptography: a novel hybrid cipher design merging Feistel and SPN structures

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments.<p> </p>

Advancing cryptographic security: a novel hybrid AES-RSA model with byte-level tokenization

As cyberattacks are getting more complex and sophisticated, stringent, multi-layered security measures are required. Existing approaches often rely on tokenization or encryption algorithms, both of which have drawbacks. Previous attempts to ensure data security have primarily focused on tokenization techniques or complex encryption algorithms. While these methods work well on their own, they have proven vulnerable to sophisticated cyberattacks. This research presents new ways to improve data security in digital storage and communication systems. We solve data security issues by proposing a multi-level encryption strategy that combines double encryption technology along with tokenization. The first step in the procedure is a byte-level byte-pair encoding (BPE) tokenizer, which tokenizes the input data and adds a layer of protection to make it unreadable. After tokenization, data is encrypted using Rivest–Shamir–Adleman (RSA) to create a strong initial level of security. To further enhance security, data encrypted with RSA has an additional layer of encryption applied using the advanced encryption standard (AES) method. This article describes how this approach is implemented in practice and shows how it is effective in protecting data at a higher level than single-layer encryption or tokenization systems.