Cryptographic Techniques Research Articles

Securing Cloud Environments by Analyzing Cryptographic Techniques for Data Integrity and Confidentiality

With cloud computing, you'll effectively and rapidly reach a parcel of computer assets. Usually exceptionally valuable in this computerized age. But this alter within the way things work makes it much harder to keep data secure and private. There are numerous reasons why security is critical for cloud settings. This exposition centers on ways to keep information safe from individuals who shouldn't be able to see or alter it. We see at a few of the foremost vital thoughts in cryptography, such as hashing, advanced marks, symmetric and hilter kilter encryption, and how they can be utilized to security the cloud. A few sorts of encryption, like AES and others, keep information secure whereas it's being sent. Other sorts of encryption, like RSA and others, keep information secure whereas it's being put away. Hash calculations, like SHA-256, deliver each set of information a one of a kind advanced stamp that creates it simple to spot changes. This makes beyond any doubt that the information is rectify. Indeed more security is included by computerized marks, which check the data's clarity and reliability to create beyond any doubt it wasn't changed whereas it was being sent. There are more complicated secure strategies that we see at, such as secure multi-party computation and homomorphic encryption. There is solid protection indeed in places where no one can be trusted since these let forms be done on secured information without giving out the essential data. This paper examines how these safety measures can be utilized in cloud-based systems and talks about how well they perform and any speed issues they might lead to. There are case studies from real life that show how safe methods have been used successfully in the cloud. This shows that they can be used in real life. We also talk about the issues that come up when you need to manage encryption keys, which is a key part of keeping data safe. A lot of attention is paid to strong key management tools, such as Hardware Security Modules (HSMs) and key management services (KMS).

Security protocol for securing notifications about dangerous events in the agglomeration

Our everyday lives cannot function without intelligent devices, which create the so-called Internet of Things networks. Internet of Things devices have various sensors and software to manage the work environment and perform specific tasks without human intervention. Internet of Things networks require appropriate security at various levels of their operation. In this article, we present a new security protocol that protects communication in IoT networks and enables interconnected devices to communicate and exchange information to increase the security of people living in urban agglomerations. The Control Station device evaluates the collected data about events that may threaten the life or health of residents and then notifies the Emergency Notification Center about it. The protocol guarantees the security of devices and transmitted data. We verified this using automatic verification technology, formal verification using Burrows, Abadi and Needham logic and informal analysis. The proposed protocol ensures mutual authentication, anonymity and revocation. Also, it is resistant to Man-in-the-middle, modification, replay and impersonation attacks. Compared to other protocols, our solution uses simple cryptographic techniques that are lightweight, stable and do not cause problems related to high communication costs. It does not require specialist equipment, so we can implement it using typical hardware. At each stage of protocol execution, communication occurs between two entities, so it does not require interaction between different entities, which may limit its adaptability in the context of interoperability.

CryptoGenSec: A Hybrid Generative AI Algorithm for Dynamic Cryptographic Cyber Defence

As the world of cybersecurity constantly changes, traditional cryptographic techniques have faced limitations in the context of today's sophisticated and dynamic threats. Existing protections usually adopt static algorithms and key structures, making it difficult for them to resist the categories of modern attacks. This research paper, therefore, presents CryptoGenSec, a brand-new generative AI algorithm based on a hybrid consisting of generative adversarial networks (GANs) on reconnaissance learning (RL), for the purpose of increasing cryptographic cyber defences. CryptoGenSec applies a GAN to simulate various types of attack scenarios in cyberspace to perceive possible vulnerabilities. Then, RL refines the response strategies of our algorithm through recursive learning from the above simulations in real time and realizes the dynamic adaptation and evolution of defense mechanisms. By assessing the results of CryptoGenSec’s performance when traditional security methods are used as baselines, we can use several metrics for evaluation, such as detection accuracy, response time, resilience and evolution ability. According to these findings, the superiority of CryptoGenSec over conventional mechanisms becomes evident. To be more specific, it even shows an overwhelming edge in terms of threat detection, resulting in a 20% increase in speed of response, a 30% decrease in speed of response, and resisting power, making it 25% harder than the other methods. Moreover, it has a greater possibility of eliminating false-positive effects, which usually come from new and even dawned jeopardy: 50%. Moreover, to highlight the making-a-fortune frauds in the zero-day world, a comparison of the cohorts makes CryptoGenSec a 40% upper step. Stopping attackers from taking away all their data is also its plus point, which gains 95% achievement, whereas using mere methods only results in a 70% possibility. An enormous step in cybersecurity was taken with the combination of GANs and RL within the CryptoGenSec algorithm. Instead of being defenceless against all attacks, this approach changes and matches the threat level when necessary. The highly promising results presented here demonstrate its potential as a crucial technology for addressing the growing complexities of cyber challenges. This is a large step toward making defensive mechanisms more efficient and reliable.

Finding Complete Impossible Differential Attacks on AndRX Ciphers and Efficient Distinguishers for ARX Designs

The impossible differential (ID) attack is one of the most important cryptanalytic techniques for block ciphers. There are two phases to finding an ID attack: searching for the distinguisher and building a key recovery upon it. Previous works only focused on automated distinguisher discovery, leaving key recovery as a manual post-processing task, which may lead to a suboptimal final complexity. At EUROCRYPT 2023, Hadipour et al. introduced a unified constraint programming (CP) approach based on satisfiability for finding optimal complete ID attacks in strongly aligned ciphers. While this approach was extended to weakly-aligned designs like PRESENT at ToSC 2024, its application to ARX and AndRX ciphers remained as future work. Moreover, this method only exploited ID distinguishers with direct contradictions at the junction of two deterministic transitions. In contrast, some ID distinguishers, particularly for ARX and AndRX designs, may not be detectable by checking only the existence of direct contradictions.This paper fills these gaps by extending Hadipour et al.’s method to handle indirect contradictions and adapting it for ARX and AndRX designs. We also present a similar method for identifying zero-correlation (ZC) distinguishers. Moreover, we extend our new model for finding ID distinguishers to a unified optimization problem that includes both the distinguisher and the key recovery for AndRX designs. Our method improves ID attacks and introduces new distinguishers for several ciphers, such as SIMON, SPECK, Simeck, ChaCha, Chaskey, LEA, and SipHash. For example, we achieve a one-round improvement in ID attacks against SIMON-64-96, SIMON-64-128, SIMON-128-128, SIMON-128-256 and a two-round improvement against SIMON-128- 192. These results significantly contribute to our understanding of the effectiveness of automated tools in the cryptanalysis of different design paradigms.

Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework

Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework

Advanced Cybersecurity Enhancement through Mathematical Models: A Comprehensive Approach Using Statistical Methods, Cryptographic Techniques, and Machine Learning Algorithms (SVM, Random Forest, and Neural Networks) for Behavior-Based Malware Detection

Advanced Cybersecurity Enhancement through Mathematical Models: A Comprehensive Approach Using Statistical Methods, Cryptographic Techniques, and Machine Learning Algorithms (SVM, Random Forest, and Neural Networks) for Behavior-Based Malware Detection

Optimizing blockchain for healthcare IoT: a practical guide to navigating scalability, privacy, and efficiency trade-offs

The adoption of blockchain technology provides significant disruptive benefits to internet-of-things (IoT) applications in healthcare in vital aspects like security, integrity, transparency, and efficiency. Nevertheless, in order to fully realize the potential of blockchain-driven solutions, healthcare organizations have to address intricate compromises between essential factors including scalability, privacy and resource utilization considering that the data sensitivity alongside strict regulatory compliance requirements characterize this sector. This research discusses the fundamental aspects of these trade-offs, including the range of consensus protocols (e.g. proof-of-work, proof-of-stake) and cryptographic techniques (e.g. zero-knowledge proofs, homomorphic encryption). A systematic choice matrix is created, which relates specific use cases of the healthcare IoT to the optimal tailored blockchain structures on such critical metrics as transaction volume, frequency, privacy level and resource restrictions. The suggested framework provides solid, actionable recommendations to healthcare organizations in order to help them benefit from the enormous promise of the blockchain for connected IoT healthcare by finding a balance between decentralization advantages and performance, security and compliance requirements.

NFTs for 3D Models: Sustaining Ownership in Industry 4.0

Digital manufacturing (DM) is actively adopted to the production lifecycles of a variety of critical industries, and this rapid growth has resulted in exponential increase of 3D computer-aided design (CAD) models. Unfortunately, counterfeiting of intellectual property becomes a prominent threat as many 3D designs are accessible online, combined with the proliferation of cheap consumer 3D printers that enable malicious actors to produce non-authentic parts. State-of-the-art techniques to secure manufacturing processes mostly rely on watermarking, which embeds hidden information inside CAD models to prove ownership and authenticity. Nevertheless, such techniques tamper with the model itself, while existing attacks allow removing such watermarks altogether. To address these shortcomings, we integrate signal processing and cryptographic techniques and describe a tailored solution for CAD model ownership and supply chain management. Our approach generates unique identifiers for 3D designs using frequency-domain transforms and employs non-fungible tokens (NFTs) that persist on public distributed ledgers. Our NFTs are implemented on the Ethereum blockchain using smart contracts and their functionality is twofold: (a) authenticate the owner of a CAD model, and (b) enable ownership transfer. To validate our technique, we deployed our smart contract on Ethereum's proof-of-work Ropsten network and demonstrated the applicability of our methodology.

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Anamoly based intrusion detection using ensemble machine learning and block-chain

<p>A major issue facing the quickly evolving technological world is the surge in security concerns, particularly for critical Internet-of-Things (IoT) applications like health care and the military. Early security attack detection is crucial for safeguarding important resources. Our research focuses on developing an anomaly-based intrusion detection system (IDS) using machine learning (ML) models. With the use of voting strategies, Bagging Ensemble, Boosting Ensemble, and Random Forest, we created a robust and long-lasting IDS. The F1 score is a crucial metric for measuring accurate predictions at the class level and serves as the focus of these ML systems. Maintaining a high F1 score in critical applications highlights the constant need for development. Make use of the latest CICIoT2023 data-set employ Hyper-ledger Fabric to create a private channel in order to bolster the security of our IDS through the usage of block-chain technology. We use block-chain's immutable record and cryptographic techniques to establish a decentralized, tamper-proof environment. Consequently, our proposed approach provides an efficient intrusion detection system that significantly enhances resource protection and alerting the user in prior with intruder information incritical regions for Internet of Things security applications.</p>

Enhancing cloud storage security through blockchain-integrated access control and optimized cryptographic techniques

Enhancing cloud storage security through blockchain-integrated access control and optimized cryptographic techniques

Hybrid Elephant Herding Optimization Approach for Cluster Head Selection And Secure Data Transmission In Wsn Using Hybrid Approach Cryptography Techniques

Introduction: The wireless nature of sensor networks makes safe transfer of data from one node to another a major challenge in communications. Sensing tasks connect these sensor nodes which have limitations of memories and energies. Cryptography techniques are utilised to handle critical issues of security in these networks. The performance of large-scale networks is enhanced in this case by optimisation algorithm mimicking natural behaviours.Methods: This work uses H-EHO (Hybrid Elephant Herding Optimisation technique based on Individual strategies to enhance cluster head selections in WSNs (Wireless Sensor Networks) and thus extend networks’ lifetime. WSNs complete cluster head selection processes, and proposed optimisation approach which selects cluster heads based on tracking of sensor nodes for enhancements. The clan operators of optimisation algorithms are adjusted to handle random walk scale factors of elephants. Clusters of WSNs elect updated sensor nodes in principle. Hybrid algorithm HSR19, a novel security symmetric technique offers greater security during data transfers. It offers integrity, confidentiality, and authentication for cryptographic primary keys. Results: The output of the simulation demonstrates the energy consumption, network longevity, end to end delay, and secure data transfer metrics. The results for choosing an effective and time-efficient cluster head selection process for WSNs are improved by contrasting the two approaches. Conclusion: This comparison also shows the efficiency of communication devices in terms of calculation times for encoding, decoding and energies consumed for various file sizes

A Review on Ancient Cryptographic, Modern Cryptographic and Quantum Cryptographic Techniques

Cryptography has a long and fascinating history, evolving from ancient techniques to modern methods and now exploring the potential of quantum mechanics. This review paper provides a comprehensive overview of cryptographic techniques from past to present. We begin by examining ancient cryptographic techniques, tracing their origins back to 2000 B.C. when the ancient Egyptians used "secret" hieroglyphics. We also discuss evidence from ancient Greece and Rome, such as secret writings and the famous Caesar cipher [1]. These early methods laid the foundation for the field of cryptography. Next, we delve into modern cryptographic techniques that have become increasingly complex and diverse in their applications. We explore how cryptography now makes extensive use of mathematical concepts from fields like information theory, computational complexity, statistics, combinatorics, abstract algebra, number theory, and finite mathematics. We also discuss how the development of digital computers and electronics has revolutionized cryptography, allowing for the encryption of any kind of binary data and the design of much more intricate ciphers [2]. Finally, we examine the emerging field of quantum cryptography, which aims to harness the principles of quantum mechanics to achieve new cryptographic functionalities beyond classical information alone. We survey some of the most remarkable theoretical uses of quantum information for cryptography, as well as the limitations and challenges faced by cryptographers in this new paradigm [3]. By tracing the evolution of cryptography from ancient times to the present day and beyond, this review paper provides valuable insights into the rich history and promising future of this critical field of study.

Review on lightweight cryptography techniques and steganography techniques for IoT environment

Review on lightweight cryptography techniques and steganography techniques for IoT environment

A novel adaptive weight bi-directional long short-term memory (AWBi-LSTM) classifier model for heart stroke risk level prediction in IoT.

Stroke prediction has become one of the significant research areas due to the increasing fatality rate. Hence, this article proposes a novel Adaptive Weight Bi-Directional Long Short-Term Memory (AWBi-LSTM) classifier model for stroke risk level prediction for IoT data. To efficiently train the classifier, Hybrid Genetic removes the missing data with Kmeans Algorithm (HKGA), and the data are aggregated. Then, the features are reduced with independent component analysis (ICA) to reduce the dataset size. After the correlated features are identified using the T-test-based uniform distribution-gradient search rule-based elephant herding optimization for cluster analysis (GSRBEHO) (T-test-UD-GSRBEHO). Next, the fuzzy rule-based decisions are created with the T-test-UDEHOA correlated features to classify the risk levels accurately. The feature values obtained from the fuzzy logic are given to the AWBi-LSTM classifier, which predicts and classifies the risk level of heart disease and diabetes. After the risk level is predicted, the data is securely stored in the database. Here, the MD5-Elliptic Curve Cryptography (MD5-ECC) technique is utilized for secure storage. Testing the suggested risk prediction model on the Stroke prediction dataset reveals potential efficacy. By obtaining an accuracy of 99.6%, the research outcomes demonstrated that the proposed model outperforms the existing techniques.

Review of Cryptography Technique of Security for Data Sharing in Clouds

Review of Cryptography Technique of Security for Data Sharing in Clouds

Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges

The rapid progression of quantum computing has created new opportunities for advancing blockchain technology, particularly in addressing its two primary challenges which are scalability and security. As blockchain adoption grows, traditional consensus mechanisms reveal significant inefficiencies, causing transaction bottlenecks and increased latency. Additionally, the advent of quantum computing poses a significant threat to the classical cryptographic algorithms foundational to blockchain security. This research introduces an innovative approach that harnesses quantum computing to address these dual challenges. To enhance scalability, this research proposes integrating Grover's algorithm and Post Quantum encryption alogrithms to optimize transaction selection and validation, significantly reducing time complexity compared to classical methods. On the security front, the research introduces post-quantum cryptographic techniques to strengthen blockchain networks against quantum adversaries. Simulations conducted on IBM Qiskit Quantum Computer Simulator demonstrate that the proposed framework achieves superior efficiency, cost-effectiveness, and faster transaction processing with Grover's algorithm and Post Quantum encryption alogrithms compared to the current classical systems. These improvements highlight the potential benefits of utilizing quantum computing to deploy quantum-enhanced blockchain. Keywords: Quantum, Computing, IBM, Qiskit, blockchain, Post-Quantum, Encryption CISDI Journal Reference Format Gabriel, O., Olabiyisi, S.O., Ismaila, W.O., Falade, O.A., & Alawode O. (2024): Integrating Quantum Computing Into Blockchain: Strategies for Overcoming Scalability and Security Challenges. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 3, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N3P2 Available online at www.isteams.net/cisdijournal

Designing a Robust Quantum Signature Protocol Based on Quantum Key Distribution for E-Voting Applications

The rapid advancement of internet technology has raised attention to the importance of electronic voting in maintaining democracy and fairness in elections. E-voting refers to the use of electronic technology to facilitate the casting and counting of votes in elections. The need for designated verification arises from concerns about voter privacy, auditability, and the prevention of manipulation. Traditional e-voting systems use cryptographic techniques for security but lack verifiable proof of integrity. Integrating e-voting with a quantum designated verifier could address these challenges by leveraging the principles of quantum mechanics to enhance security and trustworthiness. In light of this, we propose a quantum e-voting scheme that uses a designated verifier signature. To ensure the confidentiality and authenticity of the voting process, the scheme uses quantum features like the no-cloning theorem and quantum key distribution. The proposed scheme has security properties like source hiding, non-transferability, and message anonymity. The proposed scheme is resistant to many quantum attacks, such as eavesdropping and impersonation. Due to designated verification, the scheme minimizes the risk of tempering. This paper provides a detailed description of the proposed scheme and analyzes its security properties. Therefore, the proposed scheme is efficient, practical, and secure.

Employing Cryptographic Techniques for Data Security and Privacy Preservation

Employing Cryptographic Techniques for Data Security and Privacy Preservation

Creating a Logic Divider Based on BCD and Utilizing the Vedic Direct Flag Method

Reversible logic has potential for a variety of applications demanding low energy usage since it prevents information loss and energy waste. The purpose of this work is to design a new Vedic divider circuit with reversible gates. Efficiency in quantum and ASIC parameters is demonstrated by the Reversible Direct Flag Vedic Division Method (RDFVDM), which has been devised. Block-level reversible gates are used in the RDFVDM to provide benefits including lower quantum costs and less trash outputs. The performance of Cadence EDA Tool is validated by simulation trials. Based on a comparative examination utilizing current methodologies, RDFVDM performs better than comparable designs. Interestingly, it improves energy usage by 26%. Moreover, RDFVDM performs exceptionally well in terms of quantum cost while employing the RSA cryptographic technique, efficiently managing 1276,293 constant inputs and 311 garbage outputs.