The education management model refers to the system and processes that colleges and universities use to manage and oversee their academic programs and operations. However, with the advent of digital technologies, there has been a growing trend towards the Internet+ college education management model, which integrates digital technologies into all aspects of college education management. This model includes the use of online learning platforms and tools, such as learning management systems (LMS), to deliver courses and manage student progress. It also includes the use of digital technologies for administrative tasks such as admissions, enrolment, and financial aid. However, the educational management model is subjected to the challenge of security for educational data management. Hence, this paper constructed a secure framework model of the Ethereum SDN Cloud Architecture (ESDNarc). The ESDNarc model uses the Software-defined Network (SDN) for the decentralized management of the network, secure transactions, and improved efficiency. The ESDNarch model incorporates the SDN with the cryptography scheme the secure the data. The constructed model uses the double-hashing Elliptical Curve Cryptography (DHECC) for the data stored in the Ethereum blockchain. The performance of the constructed model is evaluated with the KDD data set. Simulation analysis stated that ESDNarch significantly increases the data security in the cloud model for the attacks in the network.
The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption.
More and more millennials are interested in investing in crypto assets like Bitcoin and Ethereum. However, there is still uncertainty and hesitation in making this investment. This study aims to determine the effect of herding behavior on the millennial generation's intention to invest in crypto assets. This research was conducted in Indonesia with the respondents being the millennial generation who adopted crypto assets. The population in this study is the millennial generation of crypto asset adopters throughout Indonesia which continues to change every time, so the number is unknown. The sampling technique was carried out by purposive sampling with a sample of 220 respondents. Data was collected by distributing questionnaires via google form. The analysis used is SEM-PLS. The results of this study indicate that herding on social media environment has a positive and significant effect on behavioral intention. Herding on social media environment has a positive and significant effect on financial literacy. Herding on social media environment has a positive and significant effect on E-trust. Financial literacy has a positive and significant effect on behavioral intention. E-trust has no effect on behavioral intention. Ethical concern is able to moderate the influence of herding on social media environment on behavioral intention.
Blockchain is a promising technology that is quickly gaining traction in the realm of security that is regulated by both governmental and commercial organizations. Donors are unable to know whether their donations are being used effectively due to a complete lack of transparency in donation-related transactions, which has prompted many to stop believing in charities. The immutability, traceability, and reliability properties of blockchain technology make it a viable solution for enhancing efficiency and transparency for charity. This research work is based on the Ethereum Blockchain, the decentralized donation tracking system that will permit transparent accountability, openness, and direct communication with the intended targets. The blockchain network would be made up of well-known, reliable, and esteemed companies.
Farman Ullah Khan, Faridoon Khan, Parvez Ahmed Shaikh
Abstract The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms, like neural network autoregressive (NNETAR), cubic smoothing spline (CSS), and group method of data handling neural network (GMDH-NN) algorithm. The data used in this study is spanning from April 14, 2017, to October 30, 2020, covering 1296 observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their predictive power in terms of forecasting accuracy. The predictive capabilities of CSS, NNETAR, and GMDH-NN are compared and evaluated by mean absolute error (MAE) and root-mean-square error (RMSE). Regarding the return volatility of Bitcoin and XRP markets, the forecasted results remarkably suggest that in contrast to rival approaches, the CSS can be an effective model to boost the predicting accuracy in the sense that it has the lowest forecast errors. Considering the Ethereum markets’ volatility, the MAE and RMSE associated with NNETAR are smaller than the MAE and RMSE of CSS and GMDH-NN algorithm, which ensures the effectiveness of NNETAR as compared to competing approaches. Similarly, in case of Tether markets’ volatility, the corresponding MAE and RMSE reveal that the GMDH-NN algorithm is an efficient technique to enhance the forecasting performance. We notice that no single tool performed uniformly for all cryptocurrency markets. The policymakers can adopt the model for forecasting cryptocurrency volatility accordingly.
Ring signcryption with no group administrator satisfies the decentralization and blockchain anonymity. In this article, we construct new lattice-based ring signcryption scheme suitable for consortium blockchain (CB-LRSCS), in which the smart contract controls the process of signcryption and unsigncryption to make the system be fair and reliable. CB-LRSCS can protect the user privacy by reducing the connection between blockchain and user information, and it satisfies the reliability in ethereum environment. CB-LRSCS also has the characteristics of high efficiency, anti-quantum, anti-forgery, confidentiality and unconditional anonymity, and it can be applied in the electronic finance system.
The COVID-19 pandemic has resulted in increased cross-sector cyber-attacks. Passive and reactive cybersecurity techniques relying solely on technology are insufficient to combat sophisticated attacks, necessitating proactive and collaborative security measures to minimize attacks. Cybersecurity Information Sharing (CIS) enhances security via proactive and collaborative cybersecurity information exchange, but its implementation via cloud services faces threats from man in the middle (MITM) and distributed denial of service (DDoS) attacks, as well as a vulnerability in cloud storage involving centralized data control. These threats and vulnerabilities result in a lack of user confidence in the confidentiality, integrity, and availability of information. This paper proposes Secure Cybersecurity Information Sharing (SCIS) to secure Cybersecurity Information in sectoral organizations using the private interplanetary file system (IPFS) network and the private Ethereum Blockchain network. Private Ethereum Blockchain enables secure and transparent transaction logging, while Private IPFS network provides decentralized storage, addressing vulnerabilities in centralized storage systems. The outcomes of the tests reveal that the suggested SCIS system offers cybersecurity information availability, confidentiality, and integrity. SCIS provides a high level of security to protect cybersecurity information exchanged between sectoral organizations using the Private Ethereum Blockchain network and the Private IPFS network so that organizations can safely share and utilize information.
As blockchain technology becomes more and more popular, a typical financial scam, the Ponzi scheme, has also emerged in the blockchain platform Ethereum. This Ponzi scheme deployed through smart contracts, also known as the smart Ponzi scheme, has caused a lot of economic losses and negative impacts. Existing methods for detecting smart Ponzi schemes on Ethereum mainly rely on bytecode features, opcode features, account features, and transaction behavior features of smart contracts, which are unable to truly characterize the behavioral features of Ponzi schemes, and thus generally perform poorly in terms of detection accuracy and false alarm rates. In this paper, we propose SourceP, a method to detect smart Ponzi schemes on the Ethereum platform using pre-trained models and data flow, which only requires using the source code of smart contracts as features. SourceP reduces the difficulty of data acquisition and feature extraction of existing detection methods. Specifically, we first convert the source code of a smart contract into a data flow graph and then introduce a pre-trained model based on learning code representations to build a classification model to identify Ponzi schemes in smart contracts. The experimental results show that SourceP achieves 87.2% recall and 90.7% F-score for detecting smart Ponzi schemes within Ethereum's smart contract dataset, outperforming state-of-the-art methods in terms of performance and sustainability. We also demonstrate through additional experiments that pre-trained models and data flow play an important contribution to SourceP, as well as proving that SourceP has a good generalization ability.
Afees A. Salisu, Ahamuefula E. Ogbonna, Tirimisiyu F. Oloko
This study examines the effect of pandemic-induced uncertainty on cryptocoins (Bitcoin, Ethereum and Ripple). It employs the Westerlund and Narayan (2012, 2015) predictive model to examine the predictability of pandemic-induced uncertainty and our model's forecast performance. We examine the role of asymmetry in uncertainty and the sensitivity of our results to the recently-developed Salisu and Akanni (2020) Global Fear Index. Cryptocoins act as a hedge against uncertainty due to pandemics, albeit with reduced hedging effectiveness in the COVID-19 period. Accounting for asymmetry improves predictability and model forecast performance. Our results may be sensitive to the choice of measure of pandemic-induced uncertainty.
Chengzu Dong, Jingwen Zhou, Qi An, Frank Jiang · 6 authors
The rapid advancements in technology have led to the emergence of innovative concepts like Unmanned Aerial Vehicle (UAV) delivery systems, the Metaverse, and Blockchain. As these technologies gain traction, their integration is expected to revolutionize various industries. In this paper, we propose a Blockchain-based Metaverse UAV delivery solution and a secure consensus protocol for UAV deliveries in Metaverse. Also, we conduct throughput tests using Ethereum permissioned chain. The results are promising, demonstrating that drone delivery will be an important element in future Metaverse shopping experiences. This research investigates the convergence of UAV delivery systems, the Metaverse, and Blockchain, and their implications for the future of virtual shopping experiences. The study investigates the opportunities and challenges arising from the integration of these technologies and assesses their potential synergies through the literature review and real-world case studies. Also, the experimental results demonstrate the effectiveness of our proposed architecture and consensus algorithm for drone deliveries, highlighting its ability to handle increasing network loads and maintain optimal performance.
Criptocurrency is a digital currency in which transactions are verified and recorded by a decentralized system using cryptography, rather than by a centralized authority. This ones attracted people from simple investors to academic people to do many researches about how this market works and how can be a modern alternative to our actual currency market which is formed from currencies of each traded country. This market started to develop in august 2008 and has a real growth in 2011 when apeared other criptocurrencies near principal currency – Bitcoin. After that, in january 2021 on this market the global cap exceedes 1 trillion dollars and was in a continue growth durring the first qurter of 2021 until his global cap reached the number of 2 trillion dollars. After all, in 2015 apeared the first mention about NFT in blockchain register of Ethereum. This ones has a special particularity compared to existing cryptos until then- the lack of need to store or hold an asset, which leads to a certain degree of anonymity south by a large number of investors, and the speed of training of assets. Therefore, the goal of this is to find the main factors that determine the growth of investments in this market, especialy when this market is a little bit sensitive after the fall from May 2022, when Terra token fell in 48 hours by approximately 99.8 %. In this context, the results once again convinced us that many of investors see this market an oportunity to invest where and when they want, to make fast transactions whithout paying exorbitant bank fees, anonymity of transactions and last but not least curiosity.
Abstract This entire system stands upon blockchain technology. The system is initially broken down in sections like different modules for patient, doctor, pharmacy and insurance agent. It is completed utilising a personal blockchain from Corda and Ethereum called Ganache as well as a private blockchain. This supports the development, testing, and general deployment of dApps. An Ethereum development framework is called Truffle Suite. Completely control the lifetime of modern contracts that includes bespoke installations, linking with different libraries and intricate applications on Ethereum. In dApps, Meta Mask is usefull for account creation, login and bitcoin transcations. The storage of all huge files and documents is done using the IPFS technology. Because IPFS is a P2P network circuit for decentralised data storage and the produced hash is simple to store on the blockchain, it is used. ReactJS was used to build the system’s front end. The Remix online compiler for Solidity is used to build and test smart and modern contracts that are written in the Solidity blockchain programming language.
Smart contracts, which automatically execute on decentralized platforms like Ethereum, require high security and low gas consumption. As a result, developers have a strong demand for semantic code search tools that utilize natural language queries to efficiently search for existing code snippets. However, existing code search models face a semantic gap between code and queries, which requires a large amount of training data. In this paper, we propose a fine-tuning approach to bridge the semantic gap in code search and improve the search accuracy. We collect 80 723 different pairs of <comment, code snippet> from Etherscan.io and use these pairs to fine-tune, validate, and test the pre-trained CodeBERT model. Using the fine-tuned model, we develop a code search engine specifically for smart contracts. We evaluate the Recall@ k and Mean Reciprocal Rank (MRR) of the fine-tuned CodeBERT model using different proportions of the fine-tuned data. It is encouraging that even a small amount of fine-tuned data can produce satisfactory results. In addition, we perform a comparative analysis between the fine-tuned CodeBERT model and the two state-of-the-art models. The experimental results show that the fine-tuned CodeBERT model has superior performance in terms of Recall@ k and MRR. These findings highlight the effectiveness of our fine-tuning approach and its potential to significantly improve the code search accuracy.
Abstract Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.
Ballots security and reliability is two of the crucial things that makes digital based ballots system not widely implemented. Paper-based ballots have the downsides of environmental impact on making paper. Blockchain has one of the advantages on the security aspect where the blocks are secured using cryptographic protocol that makes it hard to be hacked. This paper will be examining the possibility of ballots implementation using a microcontroller-based ballots system. The microcontroller is used because it is cheap to implement and it has the capability to sign the blockchain transaction. This paper is a proof of concept for a smaller scale ballots system for a small organization with two candidate option. The blockchain network used in this paper is a Ethereum test network provided by Ropsten. The number of testing done is 60 times total for the two candidate. The smaller scale testing proven to be successful with the transaction confirmed successful in etherscan.io.
Abstract: The advancement of blockchain innovation has prompted the making of decentralized applications (DApps), which permit client collaboration without the utilization of mediators. One of the most generally used blockchain stages for making DApps is Ethereum, which conducts business utilizing its own cash, Ether. Using Metamask, React.js, Node.js, and Solidity, we demonstrate the design and implementation of a decentralised system for Ethereum transfer in this paper. Our technology allows users to safely move Ether between accounts without the usage of intermediaries. We use React.js and Node.js to make the web application, a program module called Metamask to communicate with Ethereum, and Solidity to make the brilliant agreements that make exchanges conceivable.
Roseline Oluwaseun Ogundokun, Micheal Olaolu Arowolo, Robertas Damaševičius, Sanjay Misra
The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.
As the significance of blockchain innovation grows and the focus on scalability intensifies, rollup technology has emerged as a promising approach to tackle these scalability concerns. Nonetheless, rollups encounter restrictions when interacting with other rollups, leading to diminished throughput, increased latency, higher fees, and a complex user experience in transactions between rollups. In this paper, we put forth a novel system that employs batch settlement techniques to augment the efficiency of transfers between rollups. Our proposed system comprises a settlement rollup responsible for batch settling transfers among rollups and a smart contract structure that carries out the settlements. Notably, we utilize a zero-knowledge proof algorithm to guarantee the computational integrity of the settlement rollup while ensuring security through Ethereum smart contracts for proof verification and settlement execution. By implementing this approach, the proposed system can effectively and securely execute asset transfers between rollups, ultimately improving their scalability and usability. Consequently, our research provides a fresh perspective on resolving the challenges of throughput, latency, and fees associated with transfer systems.
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Mr. Amit Chugh, Sharad Rana, Somya Banga, Ujjwal Sharma · 5 authors
Abstract: Verifying skills is one of the major challenges in hiring new personnel. Companies and academia occasionally struggle to identify a candidate's talents because the credentials abilities that a candidate claims are not immediately verifiable and expensive for examine. For tamper-proof data storage, Blockchains have been proposed in the literature and decentralized talent verification. However, the majority of these schemes center on storing credentials are issued through regular universities for the blockchain. From several solutions that take system of certification itself into consideration, issues like numerous times, issues like (a) scaling with a small staff, (b) homogeneity of scores across numerous evaluators, or (c) extracting sincere effort from the evaluators are unaddressed. We suggest SkillVio, a blockchain-based platform that takes into account the above listed problems. and provide a number of enticing traits. As payment from platform users, such as test takers and employers, the network rewards graders with tokens.
Javier José Díaz Rivera, Waleed Akbar, Talha Ahmed Khan, Muhammad Afaq · 5 authors
Zero Trust Networking (ZTN) is a security model where no default trust is given to entities in a network infrastructure. The first bastion of security for achieving ZTN is strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A third party can intercept a JWT, and the payload information can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT. The blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides enrollment functionality. At the same time, our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.
Abstract: This research paper explores the emerging field of crowdfunding and its integration with blockchain technology. Crowdfunding has gained significant traction as a viable alternative to traditional funding methods, empowering entrepreneurs and innovators to raise capital directly from the public. However, this fundraising model is not without its limitations, including issues of trust, transparency, and intermediaries' control over the process. In recent years, blockchain technology has emerged as a potential solution to address these challenges. Blockchain's inherent properties of transparency, immutability, and decentralization offer promising opportunities for transforming the crowdfunding landscape. This research paper presents a comprehensive analysis of the benefits and challenges associated with implementing blockchain in crowdfunding. It examines real-world use cases and discusses how blockchain enhances transparency, mitigates fraud risks, reduces transaction costs, and enables global participation. Furthermore, it explores the potential impact of blockchain on crowdfunding ecosystems, including its implications for investors, entrepreneurs, and regulatory frameworks.
Abstract With the development of blockchain, cryptocurrencies are also showing a boom. However, due to the decentralized and anonymous nature of blockchain, cryptocurrencies have inevitably become a hotbed for fraudulent crimes. For example, phishing scams are frequent, which not only jeopardize the financial security of blockchain, but also hinder the promotion of blockchain technology. To solve this problem, this paper proposes a graph neural network‐based phishing detection method for Ethereum, and validates it using Ethereum datasets. Specifically, this paper proposes a feature learning algorithm named TransWalk, which consists of a random walk strategy for transaction networks and a multi‐scale feature extraction method for Ethereum. Then, an Ethereum phishing fraud detection framework is built based on TransWalk, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection.
Over the years, Cloud Computing has become rapidly embraced due to its flexibility and cost-effectiveness. However, it also presents a number of security challenges, especially with regards to access control. Conventional access control methods, like Role-based Access Control, have limitations in terms of centralized control, lack of transparency, and susceptibility to cyber-attacks. As a result, there is a need for more efficient, transparent, and secure Access Control mechanisms in Cloud Computing environments. In this Research paper, we put forward a non-centralized and tamper-proof Access Control mechanism that uses smart contract blockchain technology to address these limitations. Our model leverages the Ethereum platform's smart contract feature to stockpile access control programs and enable secure verification of user’s access requests. The smart contract blockchain is immutable, transparent, and decentralized, which makes it resistant to tampering and provides a high degree of transparency in the access control process. Our proposed model has several advantages over traditional access control mechanisms. Firstly, it provides an effective and automated approach to manage access control policies. With our model, access control policies can be easily updated and enforced through smart contracts, which eliminates the need for manual updates and reduces the risk of errors. Secondly, it provides a high degree of transparency in the access control process, which allows users to verify the legitimacy of their access requests and ensures that access control policies are being enforced fairly. Finally, it offers a heightened level of security, as the Smart Contract Blockchain is resistant to tampering and it offers a platform for Access Control that is both secure and non-centralized. To assess the efficacy of our model for Access Control management, we performed a series of experiments in a simulated Cloud Computing environment. The findings revealed that our model offers a superior and secure approach for managing access control programs compared to conventional methods. To conclude, our study suggests a secure and non-centralized access control solution by utilizing blockchain technology through smart contracts, to address the limitations of conventional Access Control methods in Cloud Computing environments. Our model provides a more efficient, transparent, and secure way to manage Access Control program to maintain the authenticity and confidentiality of Cloud services.