Cognitive radio network is prone to many malicious attacks during the process of cooperative spectrum sensing. To overcome this limitation and to efficiently use the spectrum, we incorporate block chain technology in the Fusion center in order to eliminate the malicious users and to maintain only authorized sensing results in the ledger. Fusion center takes the responsibility of smart contract ownership and organizes spectrum sensing effectively. Any malicious attack found during sensing is identified using our proposed algorithm (DETMAL-Detection of malicious users algorithm). This paper proposes a noval algorithm ’DETMAL algorithm’, which is designed in such a way that it can handle both reliant strike and self reliant strike made by the attackers. Detected attackers who are sending falsified reports are removed from the honest local decisions to make an authorised global decision. Legitimate secondary users who are the participants of the smart contract are rewarded with incentives of ethers. DETMAL algorithm shows 12% increased detection probability at 0.1 false alarm probability than the existing algorithm. Results and simulations show that our proposed model outsmarts other existing models in terms of detection probability and accuracy.
It is quite challenging to monitor goods quality or security issues due to the intricacy of a tracking system, particularly for the fundamental farming food supply chains that contribute to making up everyday feeds of individuals. There are a number of significant issues with the current agricultural food tracking systems, including a large number of subjects, challenging interactions brought on by lengthy logistics phases, and user and centrally managed distrust of data. The traceability issue in agricultural food tracking systems is successfully resolved by the development of blockchain technology. In order to reduce the requirement for central organizations and departments and enhance the dependability, safety, and authenticity of records of transactions, this work suggests a structure based on smart contracts and consortiums to monitor and document the process of agricultural food tracking system, implement transparency and accessibility, and dismantle data centers between businesses. Farmers simultaneously store file hashes in smart contracts and record environmental and agricultural development information in the InterPlanetary File System (IPFS), which improves the safety of information and mitigates the issue of blockchain storage overflow. The proposed system demonstrated superior performance with reduced delay in response (0.035) and increased transmission speed (265), reception speed (305) and processing power (17.65) compared to Ethereum and Hyperledger Fabric.
Ömer Melih Gül, Ali Hamidoğlu, Muhammad Khurram Khan, Shui Yu
Advances and growth of the Internet of Drones (IoD) allow devices, applications, and people to communicate and share data, improving comfort in daily life, commercial, and search-and-rescue operations in smart cities. This article investigates security issues in the IoD by considering both physical threats and mainly cybersecurity threats. Blockchain is considered as a promising technology for tackling security threats in IoD. Nevertheless, many distributed ledger systems do not have any low transaction latency, because the consensus protocols have to synchronize the whole blockchain. This article presents a novel game-theoretical approach, Stackelberg-followed Nash Game, for multirobot task allocation via secure communications in IoD.Finally, several challenges and future research trends are discussed about blockchain applications in uncrewed aerial vehicles.
Lo‐Yao Yeh, P. C. Chiu, Guan Yu Chen, Jiun‐Long Huang · 5 authors
Distributed Denial of Service (DDoS) attacks have emerged as a significant concern for numerous platforms, websites, and servers in recent years. Employing a blacklist access control mechanism stands out as a viable defense strategy against such attacks. However, there exists a hesitation among some Security Operation Centers (SOC) engaged in gathering threat intelligence to share their knowledge, primarily due to a lack of incentives. Therefore, advocating for a fair and effective method of sharing threat intelligence emerges as a promising research area. In this paper, we put forth a proposal for a Web3-based mechanism, outlining a threat intelligence sharing scheme that incorporates incentives while ensuring transparency through the maintenance of exchange records on smart contracts for fair reward. To uphold data confidentiality, the suggested system incorporates a proxy re-encryption scheme, which maintains data in an encrypted form during exchange, thereby preventing data leakage to unauthorized persons. Additionally, our innovative scheme incorporates a novel non-false-positive-based double filter that operates without generating false positives. This advancement serves to amplify the efficiency of data requesters, enabling them to seamlessly search for pertinent information while effectively excluding irrelevant data sets and safeguarding data privacy. According to our simulation results, the execution time of proxy re-encryption in our proposed scheme demonstrates a significant improvement of approximately 65%-70% compared to other existing schemes. This enhancement underscores the efficiency and robustness of our proposed approach in mitigating the impact of DDoS attacks.
Bitcoin is the world's first decentralized cryptocurrency, using blockchain technology to secure and verify transactions. A hybrid model based on stochastic configuration network (SCN) with conformal prediction is proposed in this study. Initially, an SCN model is built, and predictions are generated by the model. Subsequently, the predicted values from SCN are fed into the conformal prediction model, resulting in the generation of confidence intervals that validate the reliability of these values. Finally, the dataset of historical Bitcoin prices sourced from Wikipedia have been utilized. The results indicate that the SCN-conformal prediction combination enhances prediction reliability.
The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency.
In recent years, Bitcoin has gained significant attention as a leading cryptocurrency, with its price volatility drawing the interest of both investors and researchers. Predicting the future price of Bitcoin is a challenging task due to its inherent unpredictability and fluctuating market dynamics. Accurate forecasting of Bitcoin's price can provide valuable insights for traders and investors to make informed decisions. This study aims to evaluate and compare the prediction accuracy of Bitcoin prices using two distinct machine learning techniques: the Novel Apriori Algorithm and Linear Regression. This study investigates the efficiency of predicting Bitcoin prices using two machine learning techniques: the Novel Apriori Algorithm and Linear Regression. The primary objective is to forecast the Bitcoin price using these algorithms and assess their prediction accuracy. A pretest power analysis was conducted with an 80 % power level and a sample size of 20, with two distinct groups. The software implementation of both algorithms yielded an accuracy of 83.85% for the Novel Apriori Algorithm and 82.70% for the Linear Regression Algorithm. Statistical analysis, using an independent sample$t$-test, indicated a negligible difference in accuracy between the two methods ($p>0.05$), with a mean difference of$\mathbf{0. 7 6 0}$. Despite this, the Novel Apriori Algorithm demonstrated a marginally higher accuracy compared to the Linear Regression Algorithm, thus suggesting its slightly better performance in forecasting Bitcoin prices.
In this work, a patient-centric paradigm utilizing IPFS (InterPlanetary File System) storage, blockchain technology, and Zero-Knowledge Proofs (ZKPs) is proposed for handling healthcare data. Traditional healthcare data management systems frequently encounter interoperability, data security, and privacy issues. The technology guarantees safe, decentralized storage and convenient access to medical records by integrating blockchain with IPFS, while ZKPs offer strong privacy protection by permitting key verification without disclosing sensitive information. A framework is proposed that allows healthcare organizations to manage decentralized, tamper-proof healthcare ledgers. Hospitals and doctors are lightweight nodes, although patient nodes may be full or lightweight nodes. The suggested model aims to manage health data through off-chain storage. The technique, which is built on IPFS, protects the blockchain architecture from problems related to scalability. ZKPs also assist in recovering the patient's keys in case they are misplaced or forgotten. With the least amount of effort, the strategy attempts to address and mitigate the patient-centric model's deficits. This method improves efficiency and confidence in healthcare data management while simultaneously giving people ownership over their data. Additionally, the healthcare system is made more resilient, scalable, and safe while protecting patient privacy by integrating blockchain technology with IPFS.
In today’s world, the safety of children is of utmost importance due to numerous compelling factors, for example, accidents and injuries. In this fast-paced lifestyle of the new age parents, they might not always be able to accompany their children everywhere therefore the need for the tracking of the child including safety considerations and also the parent’s desire to stay connected with their child and in the absence of the parents the guardian of the child can look up for the safety of the child. This paper presents an implementation of the smart bag for toddlers which is built using blockchain technology and language solidity that ensures the tracking facility of the child. blockchain is the decentralized ledger technology that provides transparency and security between the networks. Therefore we have created a digital contract known as the smart contract on blockchain technology named a smart bag for toddlers. A smart contract is a digital agreement that is signed and stored on the blockchain and executes automatically when its terms and conditions are met.
Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya Prof. Sheetal Laroiya
Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity
Worldwide diabetes prevalence is rising, which emphasizes the necessity for safe health monitoring methods. The present study investigates the potential of implementing Ring Learning With Errors (RLWE) encryption in a smart glucometer to augment security and privacy in the context of diabetes care. Sensitive health data is encrypted and stored securely with RLWE, a lattice-based cryptography method. The Ethereum blockchain is used by the smart glucometer to provide decentralized and unchangeable data management, and RLWE encryption is included for data security. Ethereum smart contracts offer strong security features by ensuring data integrity and access management. Performance criteria, such as memory utilization and encryption or decryption times, evaluate the usefulness of RLWE in real-world medical equipment. The study highlights RLWE’s effectiveness in preventing unauthorized access to sensitive data and shows notable developments in secure health information systems. The potential of blockchain technology and RLWE efficiency over LWE to improve healthcare data security and the same is highlighted by the results obtained through the various calculations done. The study advances patient care and data security in healthcare settings by promoting safe and reliable medical IoT devices through the use of advanced cryptographic algorithms and decentralized data management.
Ugochukwu O. Mathew, Demóstenes Zegarra Rodríguez, Renata Lopes Rosa, Muhammad Shoaib Ayub · 5 authors
As Healthcare 5.0 becomes more widely accepted, the healthcare industry in general is changing, leading to better patient care and more system efficiency. The degree of digital automation that introduced cutting-edge technology applications to the health sector is seriously challenged by this shift. Concerns over patient’s privacy and security are heightened by the rising digitization of healthcare institutions, which makes it harder to collaborate and share data seamlessly. The exponential growth of healthcare data necessitates effective processing and analysis in order to maximize patient outcomes and healthcare delivery. To fully realize the potential of Healthcare 5.0, healthcare practitioners, information technology specialists, data scientists, and researchers must address these complex security difficulties. Healthcare 5.0 provides a comprehensive solution to the pressing issues facing the digitalized healthcare industry by thoroughly examining its foundational principles and exploring its practical implementation through cloud computing, data analytics, and federated learning. Through healthcare federated learning, the national electronic health (e-health) policy and strategy will be legitimized. In this paper, the authors categorized and combined healthcare technology with federated learning artificial intelligence (AI) at various automation stages. The paper discussed the issues that are now facing the healthcare industry, such as security, privacy and dependability. The paper provided readers with guidance on how to use AI and federated learning to solve healthcare information system synchronization. In conclusion, the authors discussed broad security topics and future research in the healthcare management system utilizing federated learning and blockchain approach.
Eranga Bandara, Peter Foytik, Sachin Shetty, Amin Hassanzadeh
The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core.
Shamsa Kanwal, Saba Inam, Zara Nawaz, Fahima Hajjej · 6 authors
IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.
V. Sunil Kumar, S. Renukadevi, Somashekhara Reddy, R Chandramma
By providing a secure and transparent platform for the exchange and storage of medical records, blockchain technology revolutionizes data management in the healthcare sector. Patient record confidentiality and integrity are ensured by this decentralized system, which forbids unauthorized access and tampering. Smart contracts speed up processes like insurance claims and simplify administrative work by enabling automated, trustless transactions. Additionally, blockchain facilitates seamless data- sharing by facilitating communication between various healthcare systems. Through a distributed ledger, medical professionals can access a patient’s whole medical history in real time, facilitating better diagnosis and treatment. Despite challenges like regulatory worries, blockchain’s potential for the healthcare industry holds promise for improved patient outcomes, security, and efficiency ( Kumar et al., 2021 ).
In recent trends of growth in technologies, data management, maintenance of medical records, sharing of data, diagnosis of disease, and medication are the key areas where digital healthcare plays a vital role. Despite enormous improvement, handling huge amounts of data, privacy, secure sharing, accuracy, and computational speed remains challenging. Federated learning is a machine learning technology that allows distributed model training using users’ own data to train a model. The model update is done through a central server that aggregates individual users and sends a global model. This ensures privacy protection and is suitable for handling large data. Blockchain technology is a publicly distributed ledger that collects the information of nodes as blocks and sends a copy to all nodes in the network so that data transparency is maintained and secure. However, blockchain has a limitation in handling large volumes of data. In such cases, federated learning can be used with a blockchain for better performance. By integrating federated learning with blockchain, accurate prediction, computational speed, data security, privacy, and accuracy can be achieved. A comprehensive review of how various federated learning technologies can integrate with blockchain networks to achieve accuracy and efficiency is presented.
Biometric authentication has been used in applications in various environments as a secure authentication method in computing systems.When combined with blockchain technology, the security of the biometric authentication system can be further enhanced.In this paper, we propose a biometric authentication system that does not expose the original biometric information during the user's biometric authentication process by utilizing a fully homomorphic encryption.In addition, our proposed authentication system utilizes Ethereum's smart contract, which is one of the most famous public blockchains, to record the authentication log between the user and the service provider in a distributed ledger to enhance accountability and traceability.The system is designed to be used only after obtaining the consent of the biometric data subject(user) to comply with the privacy law represented by the European General Data Protection Regulation (GDPR).Finally, we show that the proposed system can process biometric information while maintaining confidentiality, integrity, and accountability of users via security analysis.The cost of maintaining the proposed system is acceptable by analyzing computation time and blockchain maintenance cost.
This study describes the creation and deployment of a state-of-the-art Artificial Intelligence system intended for online healthcare delivery. The system can be accessed via a website using desktop browsers or specialized kiosks. Based on user input, the system provides users with succinct descriptions and cures for diseases by integrating a prediction model, which is optimized using a reinforcement learning optimizer. Moreover, it enables user-specialist doctor interactions through telemedicine advice sessions and streamlines online healthcare experiences by integrating a hybrid content-based filtering recommendation system. A secure biometric identification system using fingerprint or iris readings ensures user verification and access to medical records. Moreover, the system makes it possible to retrieve electronic prescriptions written by on-site doctors, giving consumers the option to print or buy medications directly from the pharmacists. The system stores e-prescription records on the Ethereum blockchain, allowing for future validation and reference, in order to guarantee data confidentiality and integrity. By combining cutting-edge prediction and recommendation algorithms with improved accessibility, efficiency, and data security, this research advances online healthcare delivery systems.
Piyush Kumar Pareek, E. Naresh, A. Ashwitha, A.B. Shashikala · 6 authors
Information about healthcare is crucial for both individuals and service providers. It is essential to increase the safe sharing and upkeep of electronic healthcare records (EHR). To communicate health data across healthcare stakeholders, EHR systems have traditionally depended on a centralized system (such as the cloud), which may have exposed undisclosed and secret persistent intelligence. EHR has had trouble addressing the needs of many participants and approaches in relationships of separation, security, and supplementary legal requirements. Blockchain is a distributed, decentralized journal system that can offer capabilities for safe, verified, and unchangeable data transfer. Blockchain uses consistent cryptographic techniques (hashes) to construct a distributed ledger system that enables distributed activity without the need for a central authority. Due to the immutability of a blockchain network, data exploitation is challenging and obvious. We provide a a blockchain-based architecture that uses the Proof of Stake (POS) cryptographic consensus mechanism to authenticate user identification and secure EHR exchange amongst multiple electronic healthcare systems. In order to safeguard the data that was outsourced This study suggests the use of Secure Partially Homomorphic Encryption (SPHE), which allows users to multiply and divide the ciphertext. The cloud environment&s;s access control policy is more adaptable. The results demonstrate that, in terms of superpower use, authenticity, and medical data protection, the suggested technique outperforms the alternatives.
This paper presents an innovative application of blockchain technology utilizing smart contracts to offer crop recommendations tailored to the agricultural community.Leveraging Ethereum's solidity language, we developed a decentralized system enabling farmers to access real-time data on crop selections made by their peers.The system aggregates information on crop preferences and land allocations, facilitating informed decision-making for individual farmers.Our implementation ensures data integrity and transparency, crucial for fostering trust among users.Through empirical evaluation, we demonstrate the efficacy of our approach in providing accurate and timely crop recommendations, thereby enhancing agricultural productivity and sustainability.Our findings highlight the potential of blockchain technology to revolutionize traditional agricultural practices by fostering collaboration and data-driven decision-making.We conclude by discussing future avenues for research and adoption, emphasizing the scalability and accessibility of our proposed solution in addressing broader agricultural challenges.
Fictitious products have emerged as a substantial challenge in the manufacturing sector, inflicting adverse consequences on a company's financial health, reputation, and overall prosperity. Thankfully, blockchain technology provides an effective remedy for discerning fake items and verifying the legitimacy of authentic ones, all within a decentralized and widely distributed digital ledger. Quick Response (QR) codes serve as pivotal tools in the fight against fictitious goods, as each product is now furnished with a QR code that acts as a direct bridge to the blockchain system, essentially bestowing each item with a digital identification card. QR code scanners communicate with the blockchain to promptly validate whether a product is genuine or fictitious. Moreover, customers can harness this technology to track a product's journey through the supply chain and authenticate ownership details, akin to a digital breadcrumb trail that securely preserves product information and unique codes as database blocks. Considering the globalized business landscape and the perpetual advancements in technology, industrial manufacturers and distributors are wholeheartedly committed to optimizing their supply chain processes, ensuring they remain one step ahead of fictitious product proliferation and fortifying their operations against the dissemination of spurious items. In this paper we compare Different Fake product identification Methods like Barcodes, QR Codes, RFID Tags, Serial numbers with methods using Blockchain technology which is having high security, transferability & traceability throughout Supply Chain.