Fahad F. Alruwaili, Bayan Alabduallah, Hamed Alqahtani, Ahmed S. Salama · 6 authors
Blockchain (BC) and Artificial intelligence (AI) based technologies have earned a better reputation amongst the research community, especially in the medical field. BC technology has emerged as a promising solution to revolutionize the medical field by addressing challenges related to efficiency, data security, and interoperability. A BC-aided smart healthcare system leverages the immutable and decentralized nature of BC to construct a secured and transparent ecosystem to manage processes and healthcare data. It leverages the secure and decentralized nature of BC to optimize the processes, security, interoperability, and efficiency of medical data. The existing system is exposed to security attacks on healthcare data. It can be necessary to construct a real-time detection device utilizing a cyber-physical system (CPS) with BC technology in a significant way. This article designs a novel Blockchain-Enabled Smart Healthcare System using Jellyfish Search Optimization with Dual-Pathway Deep Convolutional Neural Network (JSO-DPCNN) technique. The presented JSO-DPDCNN technique exploits the concept of BC-enabled secure data transmission and DL-based diagnosis model for moneypox disease on smart healthcare monitoring. To accomplish this, the JSO-DPCNN technique uses Ethereum-based public BC to secure the privacy of healthcare images. In addition, the JSO-DPCNN technique applies a feature extraction module using DPCNN, which extracts the suitable set of features in the input images. Moreover, the multiplicative long short-term memory (MLSTM) approach was used for the disease detection process. Lastly, the JSO system can be employed for the parameter tuning of the MLSTM model. The simulation result of the JSO-DPCNN system was executed on a benchmark medical dataset. The comprehensive outcomes highlighted the significant outcome of the JSO-DPCNN approach in terms of different measures.
M. Ramalingam, G. Chemmalar Selvi, Nancy Victor, Rajeswari Chengoden · 11 authors
Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.
The broad usage of images in real-time applications demands a cloud infrastructure due to its advantages. Many use cases are built where the image data is shared, sharing becomes the core function, and the medical domain takes its broad advantage. The cloud is a centralized infrastructure for its all-operation usages; it depends mainly on the trusted third party to handle security concerns. Therefore, the privacy preservation of the image data or any data becomes an issue of concern. The distrusted system advantages are achieved using blockchain technology for image data security and privacy concerns. The traditional approaches of the security and privacy models raise many apprehensions as these are designed on the centralized systems of the data sharing mechanisms. It is also observed that large data files are not wisely handled, which demands building a framework model that takes image data and any other data of any size to ensure a dependable optimal security system. This paper presents a framework model to achieve optimal time complexity for securing the privacy aspects of the image data or any other data that uses space optimal file system using distributed security mechanism for both the storage and sharing of the data. The proposed framework model for optimal time complexity and security uses a duplication algorithm using stakeholder agreement to ensure efficient access control to the resources using the cryptographic approach to the Ethereum ecosystem. The performance metric used in the model evaluation includes the degree of availability and efficiency. On benchmarks, it performs well compared to the traditional cloud-built distributed systems. The quantified outcome of the proposed scheme exhibits a 42.5% of reduction in time for data repositioning, a 41.1% of reduction in time for data retrieval, a 34.8% of reduction in operational cost, a 73.9% of reduction in delay, and a 61% faster algorithm execution time in contrast to conventional blockchain method.
The process of miner selection in blockchain networks plays a significant role in maintaining the efficiency and security of the net- work. In blockchain, the consensus algorithm is crucial for miner selection. Considering the downsides of the consensus algorithm (PoW, PoS) this paper employs proof of artificial intelligence (PoAI) over proof of stake and proof of work. This paper proposes an approach for miner selection using the Proof of Artificial Intelligence (PoAI) framework, which leverages Deep Neural Networks (DNNs) to predict efficient miners for blockchain networks. This paper describes the design and implementation of our PoAI that uses DNN algorithm on a bitcoin dataset, is a combination of supervised and unsupervised learning techniques to model the be- havior of miners using suitable features such as age, number of blocks mined, transaction volume and number of transactions. The proposed methodology improves the scalability of the consensus algorithm.
Internet of Medical Things (IoMT) is on-demand research area, generally utilized in most of medical applications. Security is a challenging problem in decentralized platform while handling with medical data or images. An effective deep learning-based blockchain framework with reduced transaction cost is proposed to enhance the security of medical images in IoMT. The proposed study involves four different stages like image acquisition, encryption, optimal key generation, secured storing. The input images initially are collected in the image acquisition stage. Then, the collected medical images are encrypted using coupled map lattice (CML). This encryption process assists to preserve the input medical images from the attackers. In order to provide more confidentiality to the encrypted images, optimal keys are generated using opposition-based sparrow search optimization (O-SSO) algorithm. These encrypted images are stored using distributed ledger technology (DLT) and smart contract based blockchain technology. This blockchain technology enhances the data integrity and authenticity and allows secured transmission of medical images. After decrypting the image, the disease is diagnosed in the classification stage using proposed Recurrent Generative Neural Network (RGNN) model. The proposed study used python tool for simulation analysis and the medical images are gathered from CT images in COVID-19 dataset.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain technology has been growing at a substantial growth rate over the last decade. Introduced as the backbone of cryptocurrencies such as Bitcoin, it soon found its application in other fields because of its security and privacy features. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smart contracts. Great work has been carried out to make blockchain smart, with the integration of Artificial Intelligence (AI) to combine the best features of the two technologies. This review incorporates the conceptual and functional aspects of the individual technologies and innovations in the domains of blockchain and artificial intelligence and lays down a strong foundational understanding of the domains individually and also rigorously discusses the various ways AI has been used along with blockchain to power the healthcare industry including areas of great importance such as electronic health record (EHR) management, distant-patient monitoring and telemedicine, genomics, drug research, and testing, specialized imaging and outbreak prediction. It compiles various algorithms from supervised and unsupervised machine learning problems along with deep learning algorithms such as convolutional/recurrent neural networks and numerous platforms currently being used in AI-powered blockchain systems and discusses their applications. The review also presents the challenges still faced by these systems which they inherit from the AI and blockchain algorithms used at the core of them and the scope of future work.
With the development of blockchain technology, more and more attention has been paid to the intersection of blockchain and education, and various educational evaluation systems and E-learning systems are developed based on blockchain technology. Among them, Ethereum smart contract is favored by developers for its “event-triggered” mechanism for building education intelligent trading systems and intelligent learning platforms. However, due to the immutability of blockchain, published smart contracts cannot be modified, so problematic contracts cannot be fixed by modifying the code in the educational blockchain. In recent years, security incidents due to smart contract vulnerabilities have caused huge property losses, so the detection of smart contract vulnerabilities in educational blockchain has become a great challenge. To solve this problem, this paper proposes a graph neural network (GNN) based vulnerability detection for smart contracts in educational blockchains. Firstly, the bytecodes are decompiled to get the opcode. Secondly, the basic blocks are divided, and the edges between the basic blocks according to the opcode execution logic are added. Then, the control flow graphs (CFG) are built. Finally, we designed a GNN-based model for vulnerability detection. The experimental results show that the proposed method is effective for the vulnerability detection of smart contracts. Compared with the traditional approaches, it can get good results with fewer layers of the GCN model, which shows that the contract bytecode and GCN model are efficient in vulnerability detection.
With the rapid advancement of 5G technology, the Internet of Things (IoT) has entered a new phase of application and is rapidly becoming a significant force in promoting economic development. Due to the vast amounts of data created by numerous 5G IoT devices, the Ethereum platform has become a tool for the storage and sharing of IoT device data, thanks to its open and tamper-resistant characteristics. So, Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's lives. By modeling Ethereum transaction records as a transaction network, the account types are well identified by the Ethereum account classification system established based on Graph Neural Networks (GNNs). This work first investigates the Ethereum transaction network, Surprisingly, experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per account. This flaw may significantly impede the classification capability of GNNs, which is mostly governed by their attributes. This work proposes an Adaptive Multi-channel Bayesian Graph Attention Network (AMBGAT) for Ethereum account classification to address this difficulty. AMBGAT uses attention to enhance node features, estimate graph topology structure that conforms to the ground truth, and efficiently extract node features pertinent to downstream tasks. An extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately anticipating the graph's topology.
With the continuous advancement of edge intelligence, edge servers undertake more and more intelligent computing tasks. Nowadays, there are a large number of IoT devices in the network in idle state. For instance, the mining process for consensus of miners in blockchain such as Bitcoin causes a waste of computing resources and energy. A natural question arises: can we couple the idle computing resources of network devices to continuously and credibly share the burden of edge intelligent computing tasks in a secure manner? The answer of this paper is yes. We propose a blockchain-based IoT resource monitoring and scheduling framework that supports resource management and trusted edge computing. We analyze the security threats in all phases of distributed edge computing, and utilize the trusted computing and public verifiability features of blockchain to ensure reliability and fairness in the trusted measurement of device computing power, the decomposition of intelligent computing tasks, the matching of task and computing power, and the verification of computing result. Finally, we implement a simulation on the edge network by performing distributed machine learning task for weather prediction, and the simulation results demonstrate the availability of our scheme.
Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature.
The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.
Dalia Elwi, Osama Abu-Elnasr, A. S. Tolba, Samir Elmougy
Abstract Bitcoin is a digital cryptocurrency which had become the focus of scientific research in the modern era. Blockchain is the underlying technology of Bitcoin because of its decentralization, transparency, trust-less, and immutability features. However, blockchain can be considered the cause of Bitcoin scalability issues especially storage. Nodes in Bitcoin network need to store the full blockchain to validate transactions. By time, the blockchain size will be extremely huge. So, the full nodes will prefer to leave the network, and this leads to the blockchain being centralized and trusted. Therefore, security will be adversely affected. In this paper, we propose a Stateful Layered Chain Model which is based on storing accounts’ balances to reduce the size of the Bitcoin blockchain. This model changes the structure of the traditional blockchain from blocks to layers. The experimental results demonstrated that the proposed model reduces the size of blockchain by about 50.6%. Imlicitly, the transaction throughput can also be nearly doubled.
Syed Agha Hassnain Mohsan, Abdul Razzaq, Shahbaz Ahmed Khan Ghayyur, Hend Khalid Alkahtani · 6 authors
Several academicians have been actively contributing to establishing a practical solution to storing and distributing medical images and test reports in the research domain of health care in recent years. Current procedures mainly rely on cloud-assisted centralized data centers, which raise maintenance expenditure, necessitate a large amount of storage space, and raise privacy concerns when exchanging data across a network. As a result, it is critically essential to provide a framework that allows for the efficient exchange and storage of large amounts of medical data in a secure setting. In this research, we describe a unique proof-of-concept architecture for a distributed patient-centric test report and image management (PCRIM) system that aims to facilitate patient privacy and control without the need for a centralized infrastructure. We used an Ethereum blockchain and a distributed file system technology called the Inter-Planetary File System in this system (IPFS). Then, to secure a distributed and trustworthy access control policy, we designed an Ethereum smart contract termed the patient-centric access control protocol. The IPFS allows for the decentralized storage of medical metadata, such as images, with worldwide accessibility. We demonstrate how the PCRIM system design enables hospitals, patients, and image requestors to obtain patient-centric data in a distributed and secure manner. Finally, we tested the proposed framework in the Windows environment by deploying a smart contract prototype on an Ethereum TESTNET blockchain. The findings of the study indicate that the proposed strategy is both efficient and practicable.
Mohit Kumar, B Annappa, Likewin Thomas, Sourav Kanti Addya · 5 authors
Blockchain is essentially a decentralized framework and a transaction processing model governed by no central authority. As blockchain can be considered as a database for tracking changes similar to accounting ledger, it is often referred to as a distributed ledger. Each transaction is digitally signed as proof of authenticity, and the ledger is maintained so that once entries are added or amended, they cannot be deleted or modified by one party without detection of others. Thus, blockchain functions as an immutable ledger that enables decentralized transactions with the help of smart contracts and consensus algorithms. There are several other cryptocurrencies, such as Bitcoin, that are built on the blockchain. With BaaS (Blockchain as a Service), users can use cloud-based solutions to create, host, and manage their own blockchain applications and related tasks. Blockchain has numerous potential applications in a variety of industries such as healthcare, supply chain, logistics and much more. Identity management is one of the most promising opportunities which helps to authenticate one&s;s identity. Stellar (Blockchain based-platform) can be used for cross-border payments as it is an open source, decentralized protocol for transfer fiat currencies. Ecommerce platforms need a large volume of transactions on a scalable system and as Bitcoin cannot provide the same, a off-chain payment network Bitcoin Lightning Network is a solution to this problem. It provides fast transactions and can be used in any kind of Business or Financial Industry.
Blockchain technology considers the central technology that is used within many applications used frequently with human life. And the primary core of the blockchain is the consensus algorithm which may affect the security of the chain as well as the required resource consumption which affect mainly the blockchain performance directly. In recent years many consensus algorithms have been used and proposed such as proof of work (PoW) and proof of stake (PoS) and many others. However, these algorithms still need some improvement to the security and system resource consumption which will reduce the need for a huge amount of energy and save the environment as well as let the blockchain be useable within low computation ability devices such as the internet of things devices (IoT). This paper proposes a new consensus algorithm that ensures the integrity and authorization of nodes participating in the validation of the transaction and only a predefined number of nodes chosen randomly to participate in block addition which reduces the need for high computations power for mining and voting. The proposed algorithm needs lower time and computation costs comparable to the standard POW algorithm.
Anand Singh Rajawat, S. B. Goyal, Pradeep Bedi, Simeon Simoff · 6 authors
Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.
A. B. Pawar, M.A. Jawale, P. William, Gurpreet Singh Chhabra · 7 authors
Early identification of lung cancer is essential since the disease progresses quickly. Early-stage lung cancer diagnosis will be the first usage of the Internet of Things (IoT). With a worldwide network of IoT devices and a high degree of trust in the model's accuracy, on-the-fly training for IoT devices is very essential. As many as a million lives are saved each year because to early detection of illness, which seals the airways and prevents infection. Image processing and machine learning techniques provided the first evidence of malignant growth. Symptoms of lung cancer generally don't show up until the disease has advanced very far. At this stage, getting medical help becomes quite difficult. A whistling sound, hoarseness, weight gain in the face and/or an increase in the size of the upper chest may appear first, followed by the curling or rising of your fingers or the experience of pain when swallowing. Sputum with a red or rust-colored hue is a sign of malignancy, as is shortness of breath and chronic chest pain. In addition to identifying and arranging lung knobs, a lung computed tomography image may also be utilised to estimate their risk level. Preparation does not have as much of an impact on ECNN's accuracy and temporal complexity as it did on previous frameworks. They are made up of abnormal cells that form a tumour. An uncontrolled development and destruction of the lungs. Various kinds of lung cancer begin to develop as a result of this process, which continues until a tumour forms. Lung cells are damaged when they come into contact with airborne contaminants. + The new approach offered is ECNN+.
Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their performance is known to degrade with the presence of negative edges that may arise in many common financial applications. Considering the rich semantic inference of negative edges, excluding them as an obvious solution is not elegant. Alternatively, another basic approach is to apply positive normalization, however, this also may lead to information loss. Our work proposes a simple yet effective solution to overcome these two challenges by employing the eigenvectors with top-k largest eigenvalues of the raw adjacency matrix for pre-embeddings. These pre-embeddings contain high-order topological knowledge together with the information on negative edges, which are then fed into a GNN with a positively normalized adjacency matrix to compensate for its shortcomings. Through comprehensive experiments and analysis, we empirically demonstrate the superiority of our proposed solution in a Bitcoin user reputation score prediction task.
Telemedicine has the potential to be a good resource for early disease diagnosis, provided that it is utilised in the correct manner. The Internet of Things (IoT) is a concept that has developed in recent years as people have become more aware that they are continuously being watched. As a result of the increased prevalence of neurodegenerative disorders like Alzheimer's disease (AD), biomarkers for these conditions are in high demand for early-stage resource prognosis. Because of the precarious nature of the situation, it is absolutely necessary for these structures to offer remarkable qualities such as accessibility and precision. Deep learning strategies could be useful in fitness applications in situations in which there are a large number of data points to be analysed. Excellent data for a decentralized Internet of Things device that is based on block chain technology. By utilizing a connection to the internet that is of a high speed, it is feasible to obtain a prompt answer from these structures. It is not possible to run deep learning algorithms on smart gateway devices since they do not have sufficient computational capacity. In this study, we investigate the potential for increasing the speed of data flow in the healthcare industry while simultaneously improving data quality through the incorporation of blockchain-based deep neural networks into the control system. Experiments are being conducted to evaluate the speed and accuracy of real-time fitness tracking for the purpose of classifying groups. We are able to determine if diseases of the brain are benign or malignant by employing a model that utilises deep learning. For the purpose of determining the relative severity of each condition, the research examines the symptoms of several different mental diseases and compares them to those of Alzheimer's disease, moderate cognitive impairment, and normal cognition. The research calls for a number of different procedures. The majority of the data is used to train the classifiers, while the remainder of the data is utilised in conjunction with an ensemble model and meta classifier to classify individuals into the appropriate categories. The OASIS-three database is a long-term study that incorporates neuroimaging, cognitive, clinical, and biomarker measurements. This study focuses on healthy ageing as well as Alzheimer's disease. When comparing the outcomes of the simulation to those acquired from the real world, the OASIS-three database (AD), in addition to the ADNI UDS dataset, is employed as a comparison tool. The findings show that answers to questions about this issue can be arrived at quickly and categorized utilizing an in-depth methodology (98% accuracy).
Blockchain-enabled Internet of Things (IoT) has been receiving growing attention. However, IoT nodes are usually resources heterogeneous and subject to malicious attacks, such as the intentional delay of block verification and the transmission of invalid blocks. As a result, a random node-selection model for block transmission may lead to a low transmission rate and serious security risk. To solve the problem, this article proposes a rate-and-trust-based node selection model for block transmission. In our model, we calculate the block transmission rate of a node by the latency and connectivity among nodes and its trust value by its historical transmission and verification behaviors. On this basis, we propose a PageRank-based optimization algorithm for node selection that makes a tradeoff between the transmission rate and the security risk. Extensive experimental results show that the proposed model can achieve better performance than the state-of-the-art methods, including Bitcoin network, Ethereum network, and BlockP2P-EP protocol.
K. Kalyani, Velmurugan Subbiah Parvathy, Hikmat A. M. Abdeljaber, T. Satyanarayana Murthy · 7 authors
In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
Blockchain is one of the most advanced technologies that play an important role in many different fields such as healthcare, capital markets and logistics. Among the many existing blockchain platforms, the integration of the Turingcomplete virtual programming engine with the blockchain makes the Ethereum blockchain one of the most paramount infrastructures for various types of applications, including but not limited to cryptocurrency trading, smart contracts, decentralised finance and metaverse. Nevertheless, Ethereum like many other computing systems, has fallen victim to vector attacks that exploit its vulnerabilities and have catastrophic consequences. Out of the need to protect Ethereum from such attacks, this paper proposes a novel deep learning model based on convolutional neural networks. The proposed model treats the transaction, which is the atomic entity in this platform, as a stochastic time series and then develops two specific task layers that are compatible with the traditional CNN architecture. The first layer is responsible for detecting the seasonal characteristics of the transactions, while the second layer is used for detecting the trend. These two layers are integrated with the traditional architecture to form a powerful temporal CNN architecture that can classify different types of attacks. The performance of the proposed model was evaluated from a different perspective using real transactions collected from the Ethereum main-net network. The results of the comprehensive evaluations show the ability of the proposed model to perfectly identify malicious transactions in the Ethereum blockchain.