Muhammad Nabil Rateb, Sameh Alansary, Marwa Khamis Elzouka, Mohamad Galal
Abstract Sentiment analysis is a powerful tool for extracting valuable insights from social media data. In this paper, more than one million tweets spanning three months (March, June, and December 2022) regarding three cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) during the Russian-Ukrainian War are considered. Two models, a convolutional neural network with long short-term memory (CNN-LSTM) and a support vector machine (SVM) with GloVe and TF-IDF features, are trained on a labeled dataset of more than fifty thousand tweets about Bitcoin labeled as (positive, negative, and neutral). A pretrained model (Pysentimento) for sentiment analysis is also employed to compare the performances of the three models. The models are tested on the labeled dataset and then evaluated on the unlabeled tweets, revealing that Pysentimento's level of accuracy outperforms the other two models. Google Trends, along with the opening and closing prices, and the volume of the three cryptocurrencies, in addition to the results of Pysentimento sentiment classification, are employed to apply the Pearson correlation coefficient and conduct price prediction analysis using the SARIMA model. It is found that Bitcoin may appeal to those seeking stability and a known record of accomplishment, while Binance Coin and Ethereum may attract investors looking for more diverse opportunities. Sentiment analysis using machine learning is found to provide invaluable information for cryptocurrency price forecasting and trading strategies, especially in the context of geopolitical events and market volatility.
Khaled Mokni, Ghassen El Montasser, Ahdi Noomen Ajmi, Elie Bouri
Abstract Most previous studies on the market efficiency of cryptocurrencies consider time evolution but do not provide insights into the potential driving factors. This study addresses this limitation by examining the time-varying efficiency of the two largest cryptocurrencies, Bitcoin and Ethereum, and the factors that drive efficiency. It uses daily data from August 7, 2016, to February 15, 2023, the adjusted market inefficiency magnitude (AMIMs) measure, and quantile regression. The results show evidence of time variation in the levels of market (in)efficiency for Bitcoin and Ethereum. Interestingly, the quantile regressions indicate that global financial stress negatively affects the AMIMs measures across all quantiles. Notably, cryptocurrency liquidity positively and significantly affects AMIMs irrespective of the level of (in) efficiency, whereas the positive effect of money flow is significant when the markets of both cryptocurrencies are efficient. Finally, the COVID-19 pandemic positively and significantly affected cryptocurrency market inefficiencies across most quantiles.
Chengying He, Yong Li, Tianqi Wang, Salman Ali Shah
Abstract In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven.
Abstract Decentralized applications (DApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have recently gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each DApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the DApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the systemâs robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each DApp play a central role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.
In recent years, decentralized applications (dApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each dApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the dApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system's robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each dApp play a pivotal role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.
Manoel Fernando Alonso Gadi, Maximilian Schmidt, Noah Ruemmele, MiguelâĂngel Sicilia
Stock market indices are pivotal tools for establishing market benchmarks, enabling investors to navigate risk and volatility while capitalizing on the stock market's prospects through index funds. For participants in decentralized finance (DeFi), the formulation of a token index emerges as a vital resource. Nevertheless, this endeavor is complex, encompassing challenges such as transaction fees and the variable availability of tokens, attributed to their brief history or limited liquidity. This research introduces an index tailored for the Ethereum ecosystem, the leading smart contract platform, and conducts a comparative analysis of capitalization-weighted (CW) and equal-weighted (EW) index performances. The article delineates exhaustive criteria for token eligibility, intending to serve as a comprehensive guide for fellow researchers. The results indicate a consistent superior performance of CW indices over EW indices in terms of return and risk metrics, with a 30-constituent CW index outshining its counterparts with varied constituent numbers. The recommended CW30 index demonstrates substantial advantages in comparison to established benchmarks, including prominent indices like DeFi Pulse Index (DPI) and CRypto IndeX (CRIX). Additionally, the article explores the practicality of implementing the CW30 in Layer 2 networks of the Ethereum Ecosystem, advocating for the Arbitrum infrastructure as the optimal choice for the decentralized crypto index protocol herein referred to as the Ethereum Ecosystem Index (EEI). The study's insights aspire to enrich the DeFi ecosystem, offering a nuanced understanding of network selection and a strategic framework for implementation. This research significantly enhances the existing literature on index construction and performance within the Ethereum ecosystem. To our knowledge, it represents a pioneering comprehensive analysis of an index that accurately mirrors the Ethereum market, advancing our comprehension of its intricacies and wider ramifications. Moreover, this study stands as one of the initial thorough examinations of index construction methodologies within the nascent asset class of crypto. The insights gleaned provide a pragmatic approach to index construction and introduce an index poised to serve as a benchmark for index products. In illuminating the unique facets of the Ethereum ecosystem, this research makes a substantial contribution to the current discourse on crypto, offering valuable perspectives for investors, market stakeholders, and the ongoing exploration of digital assets.
The prevalence of maximal extractable value (MEV) in the Ethereum ecosystem has led to a characterization of the latter as a dark forest. Studies of MEV have thus far largely been restricted to purely on-chain MEV, i.e., sandwich attacks, cyclic arbitrage, and liquidations. In this work, we shed light on the prevalence of non-atomic arbitrage on decentralized exchanges (DEXes) on the Ethereum blockchain. Importantly, non-atomic arbitrage exploits price differences between DEXes on the Ethereum blockchain as well as exchanges outside the Ethereum blockchain (i.e., centralized exchanges or DEXes on other blockchains). Thus, non-atomic arbitrage is a type of MEV that involves actions on and off the Ethereum blockchain. In our study of non-atomic arbitrage, we uncover that more than a fourth of the volume on Ethereum's biggest five DEXes from the merge until 31 October 2023 can likely be attributed to this type of MEV. We further highlight that only eleven searchers are responsible for more than 80% of the identified non-atomic arbitrage volume sitting at a staggering $132 billion and draw a connection between the centralization of the block construction market and non-atomic arbitrage. Finally, we discuss the security implications of these high-value transactions that account for more than 10% of Ethereum's total block value and outline possible mitigations.
In an attempt to assess the appropriateness of the best-practice lexicon-based approaches as opposed to novel learning-based models to extract the sentiment of textual content in the context of the cryptocurrency market, the current study provides further insights into the association between digital activity and price movement of cryptocurrencies. Using a sample of Bitcoin and Ethereum trade data, this study compares the performance of Harvard IV-4 and BERT models in conjunction with the well-known machine learning classifiers. It examines to what extent learning-based sentiment models can enhance the price movement prediction, compared to lexicon-based approaches, and whether the prediction is improved or impaired by introducing different features as input to the classifiers. Results indicate that the contribution of the selected learning-based model varies across the two cryptocurrencies, and predictions are better in the absence of trade volume as an input feature to the classifiers.
Game Tamadoge mengusung konsep Play to Earn dengan menggunakan teknologi NFT (Non-Fungible Token) di atas blockchain Ethereum dan pemilik platform menawarkan perjanjian pengguna akhir kepada pengguna, untuk itu diperlukan analisis hukum dan keamanan antar para pihak. Penelitian ini menggunakan metode normatif dengan pendekatan perundang-undangan (statute approach), dan pendekatan konseptual (conceptual approach). Hasil penelitian ini adalah perjanjian pengguna akhir yang yang ditawarkan pemilik platform dapat membantu pengguna dan pemain game untuk memahami hak dan kewajiban mereka, dari mulai kepemilikan Hak Kekayaan Intelektual atas karya Non-Fungible Token, pedoman permainan, ekonomi dalam game, dan strategi, serta perjanjian yang ditawarkan memberikan perlindungan hukum yang lebih baik dalam menggunakan platform game berbasis blockchain Ethereum.
Abstract Timed-release encryption (TRE) is a cryptographic primitive that can control the decryption time and has significant application value in time-sensitive scenarios. To solve the reliability issue of nodes in existing TRE anonymous interaction schemes, we propose a blockchain-based TRE protocol for anonymous query time trapdoors. In our protocol, the recipient divides the encrypted trapdoor request information into n ciphertext fragments using secret sharing technology near the decryption time, and employs the idea of onion routing to perform layer-by-layer encryption, creating onion-type data transmitted through middlemen selected from the smart contract. After receiving the ciphertext fragments, the time server integrates them to obtain the trapdoor request information and returns the corresponding time trapdoor to the recipient. This allows the recipient to query any time trapdoor anonymously. Our protocol provides a normative design for the smart contract and specific constraints on the participantsâ behavior. Compared with the related anonymous query trapdoor schemes, our protocol improves the probability of successful queries. Security analysis shows that our protocol can resist release-ahead attack, interruption attack, eavesdropping attack, and replacement attack. Performance analysis shows that our protocol outperforms related protocols regarding anonymity, efficiency, and flexibility, achieving highly efficient anonymous interactions. Finally, we conducted an experiment in the Ethereum Rinkeby test network. For the settings of ciphertext fragment number $$n=3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:math> and ciphertext fragment threshold $$t=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>t</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> , the gas consumption for a user to execute the contract was $5.66, which was higher than the contract cost of related schemes, but the contract execution cost was within an acceptable range.
Muhammad Farrukh Shahzad, Shuo Xu, Weng Marc Lim, Muhammad Hasnain · 5 authors
Abstract Cryptocurrenciesâi.e., digital or virtual currencies secured by cryptography based on blockchain technology, such as Bitcoin and Ethereumâhave brought transformative changes to the global economic landscape. These innovative transaction methods have rapidly made their mark in the financial sector, reshaping the dynamics of the global economy. However, there remains a notable hesitation in its widespread acceptance and adoption, largely due to misconceptions and lack of proper guidance about its use. Such gaps in understanding create an opportunity to address these concerns. Using the technology acceptance model (TAM), this study develops a parsimonious model to explain the awareness, acceptance, and adoption of cryptocurrency. The model was assessed using partial least squares structural equation modeling (PLS-SEM) with a sample of 332 participants aged 18 to 40 years. The findings suggest that cryptocurrency awareness plays a direct, positive, and significant role in shaping cryptocurrency adoption and that this positive relationship is mediated by factors that exemplify cryptocurrency acceptance, namely the ease of use and usefulness of cryptocurrency. The results also reveal that trust is a significant factor that strengthens these direct and mediating relationships. These insights emphasize the necessity of fostering an informed understanding of cryptocurrencies to accelerate their broader adoption in the financial ecosystem. By addressing the misconceptions and reinforcing factors like ease of use, usefulness, and trust, policymakers and financial institutions can better position themselves to integrate and promote cryptocurrency in mainstream financial systems.
Zia Ullah, Abdul Waheed, Muhammad Ismail Mohmand, Sadia Basar · 6 authors
Artificial intelligence (AI) is one of the key technologies emerging in the Industrial Revolution that could protect against cybersecurity threats. AI is a key component of big data analytics and enables accurate real-time data analysis. AI can analyze big data, but it has some issues with security, privacy, and centralization of data. Moreover, cybercriminals continue to advance, so law enforcement faces more threats. With traditional cybersecurity solutions, sophisticated cyber-attacks are harder to detect and defend against. In complex cyberspaces, AI algorithms mine valuable features from data. However, the data on the Internet is scattered and controlled by different parties, making it challenging to authorize and validate its use. The AICyber-Chain model is presented in this paper for securely storing, calculating, and distributing data on the Internet at an enterprise scale. In a large-scale Internet environment, our proposed AICyber-Chain model integrates three key components to ensure a more secure cyberspace, enhancing AI, namely: Firstly, blockchain-based data sharing guarantees ownership at a large scale, enabling real-time data sharing. Secondly, a platform powered by AI makes cyberspace more trustworthy. Thirdly, sharing data or services rewards participants financially, which promotes sharing. We also discuss a typical use scenario, an alternative deployment method, and its security and commercial efficacy. Also, we simulated our model on Ethereumâs official test network, called Rinkeby, to demonstrate its practicality and efficiency. This model speeds up authentication by 1.8 times compared to the centralized model. In addition, our proposed solution reduces gas consumption by 20 to 25%. Our paper aims to serve as a guide and reference point for cybersecurity researchers and industry practitioners, especially from an intelligent computing or AI-based technical standpoint.
Purpose: The rapidly growing cryptocurrency market presents a potentially lucrative avenue for investment in a sector experiencing increasing demand. This study aimed to investigate the volatility of four prominent cryptocurrencies - Bitcoin, Ethereum, Tether, and BNB - and assess their impact on inflation for Indian investors. Methodology: Data on the four chosen cryptocurrencies was collected and analyzed to assess volatility margins and trading volumes during specific periods. Microsoft Excel functions were used for data retrieval, while Bollinger Bands were generated using the open-source R statistical software (version 4.4). Findings: Construction of Bollinger Bands with R software and data analysis using Excel revealed significant volatility in Bitcoin and Ethereum prices. Conversely, Tether and BNB demonstrated relatively low volatility. Practical Implications: The findings of this research offer valuable insights for academics, current and potential investors, and policymakers navigating the evolving cryptocurrency market. Originality: This study uniquely focuses on identifying cryptocurrencies with lower volatility, addressing a crucial concern for investors beyond mere utility. It stands out as a rare investigation specifically aimed at pinpointing safer cryptocurrency options for future investment.
Virtual currency assets are an important component of the international investment market. This study applies the Markowitz theory model to the data of the four main virtual currency assets in the market, Bitcoin, Tether, Ethereum, and BNB, in the past year. The Markowitz theory model is used to quantitatively analyze these four virtual currency assets and obtain the short-term data distribution of the investment portfolio when considering investing in these four virtual currency assets at the same time. In the short term, this study recommends investors who are concerned about returns to short Tether, Ethereum, and BNB virtual currencies and invest in Bitcoin and BNB virtual currencies. It is recommended that investors who are concerned about both returns and risks to short Tether, Ethereum, and BNB virtual currencies and invest in Bitcoin or BNB virtual currencies. Risk assets. This study only focuses on short-term data from the past year and provides recommendations. With the development of virtual currencies, market conditions may vary.
This paper explores the development of ERC-404 tokens within the Ethereum blockchain ecosystem and investigates strategies for enhancing digital asset growth. The paper finds effective methods for developing ERC-404 tokens and examines their effects on digital asset management through a thorough literature review and data analysis. For blockchain developers, investors, and enthusiasts looking to use ERC-404 tokens for long-term growth in the digital asset market, the findings offer insightful information. In the digital age, blockchain technology and cryptocurrencies have completely changed how we see and exchange value. Ethereum has become a prominent platform for token production and decentralised apps (DApps) among the many blockchain-based assets. Token interfaces and protocols within the Ethereum ecosystem are defined by ERC standards, which were created by Ethereum Improvement Proposals (EIPs). For example, ERC-20 tokens have become widely used due to their interoperability and fungibility. However, as the blockchain industry's requirements change, there is a growing need for specialised token standards suited to particular use cases. This paper focuses on ERC-404 tokens, a proposed standard designed to address the limitations of existing token standards and facilitate innovative digital asset management solutions.
Mohammad Khalid Imam Rahmani, Javed Ali, Surbhi Bhatia Khan, Muhammad Tahir
Ethereum has gained significant attention from businesses as a blockchain technology since its conception. Beyond the first use of cryptocurrencies, it provides many additional features. In the pharmaceutical sector, where reliable supply chains are necessary for cross-border transactions, Ethereum shows promise. It addresses problems through quality, traceability, and transparency in a place defined by complexity and strong laws because of its decentralized structure. As a result, this study looks at how Ethereum is used in the pharmaceutical sector, namely the networks that allow smart contracts to communicate with one another on the Ethereum network. The above concepts are formulated via communication networks, inter-contract owner interactions, and simulation analysis, which seeks to identify dubious practices and unjust contracts inside the supply chain. The study suggests effective manufacturing techniques that call for reduction rather than storage to technological obstacles. With this endeavor, we hope to provide insights into Ethereum-based contract ecosystems and assist in anomaly identification for enhanced security and transparency. The main objective is to support patient record methodology and transform the way healthcare data is managed. The suggested model integrates front-end interfaces, back-end optimization, distributed storage, proof-of-work techniques, and training to establish a safe and efficient ecosystem for healthcare data. These elements can be combined through the blockchain-enabled architecture to transform manufacturing-protecting chemicals in handling, distribution, and necessary training.
Purpose : It can be stated that in todayâs competitive conditions, where portfolio management is very important, it has become necessary to examine the relationship between global financial assets and major cryptocurrencies, such as Bitcoin and Ethereum. This paper aims to investigate the cointegration and causalityrelationships between Bitcoin, Ethereum, and global financial assets such as gold, oil, the S&amp;P Global 100, the Dow Jones Commodity, and the US Dollar Indices, and to determine the diversification role of Bitcoin and Ethereum comparatively for the period between April 2016 and January 2024. Methodology: The ADF Unit Root, Johansen Cointegration, Granger Causality, Rolling Window Causality tests, and Variance Decomposition Analysis methods were used in the analysis process. Results: Based on the findings obtained from the paper, it was determined that Bitcoin and Ethereum have no cointegration with selected financial asset classes. Granger causality analysis results indicated that there were unidirectional causalities from Bitcoin and Ethereum prices to Dow Jones Commodity Index prices. In addition to the results of the Rolling Window causality tests, it was also determined that there are some causalities between Bitcoin, Ethereum, and other variables, especially after the 2021-2022 period. Conclusion: It can be concluded that Bitcoin and Ethereum are effective portfolio diversifiers throughout the entire period; however, the diversification effects of Bitcoin and Ethereum weakened towards the end of the review period. Therefore, it can be said that Bitcoin and Ethereum act similarly in the global investment portfolio.
Minjae Seo, Jaehan Kim, Myoungsung You, Seungwon Shin · 5 authors
Blockchain technology has ushered in a transformative paradigm of decentralized and transparent systems, offering innovative solutions across diverse sectors. While these systems strive for unparalleled transparency and trustlessness in a fully distributed framework, permissionless blockchains, such as Bitcoin and Ethereum, encounter vulnerabilities due to their intrinsically public nature. Addressing these vulnerabilities, the emergence of permissioned blockchains presents a fortified alternative, incorporating rigorous access controls and authentication protocols to ensure participation exclusivity and transaction confidentiality. Nevertheless, a keen observation reveals that, despite encryption, the operational traffic within these blockchains manifests distinct time-series patterns and operational relations during sensitive data exchanges. Such patterns hold the potential to inadvertently expose critical details about the network, encompassing its topology and the operational dependencies among nodes. In light of this revelation, we introduce a pioneering blockchain fingerprinting mechanism, denoted as gShock. This system meticulously analyzes periodic patterns and the context of operational relations from the collected blockchain network traffic. It employs a Graph Neural Network (GNN)-based model, adept at capturing the intricate characteristics innate to specialized blockchain operations. Through empirical experiments conducted in a realistic permissioned blockchain environment, comprising various nodes, we ascertain that gShock demonstrates a remarkable proficiency in classifying blockchain operational traffic with an F1-score of$\geq 96$% and identifying individual dependencies with a macro F1-score of$\geq 93$%.
Open access
Advanced Steganography and Watermarking Techniques
Support tools that monitor software application processes are required to identify, measure, and assess their performance and resolve any anomalies that impede their full functionality. Similarly, applications that use blockchain technology require analysis and monitoring tools for the resources and functionality provided by their smart contracts. This paper presents a computational tool that monitors hardware resources and evaluates transaction processing requests in blockchain networks. The solution presented here is called MonitorChain, and it has software components that allow the connection with different blockchain networks, the configuration of the workload, the monitoring of consumed hardware resources, and the evaluation of requests processed over blockchain networks. The effectiveness and adaptability of MonitorChain were validated through extensive evaluations across various blockchain environments, including public networks like Ethereum, Avalanche, and Fantom, as well as a private network using Hyperledger Fabric, with a consistent data load of 10,000 transactions. Additionally, through interviews with experienced software developers, we gathered feedback that led to further refinements and identified opportunities for future enhancements. The results demonstrated MonitorChainâs capability to provide critical insights into blockchain applicationsâ performance, scalability, and efficiency. MonitorChain advances the field compared to existing solutions by offering enhanced support for diverse networks, customizable workloads, and real-time data visualization, contributing significantly to the blockchain monitoring landscape.
Intelligent lighting systems achieve high energy efficiency through precise control and serve as vital tools for reducing carbon emissions, providing essential data for carbon trading. However, data exchange between the lighting system and the carbon trading system presents several challenges. For instance, data may be maliciously tampered with, and frequent unauthorized access threatens the normal operation of carbon trading. Therefore, this paper proposes a security framework for intelligent lighting systems based on blockchain technology. The framework utilizes a dual-chain structure of Hyperledger Fabric and Ethereum to address the issues of blockchain storage expansion and transaction efficiency, employing smart contracts to ensure the effective processing of lighting data. The security requirements for intelligent lighting data are thoroughly studied and analyzed. Additionally, this paper presents a key distribution scheme based on the RSA encryption algorithm to ensure trusted access control within the system. Through detailed analysis and practical verification of the scheme's security and performance, the framework not only effectively prevents data tampering but also ensures data authenticity and the smooth operation of the system during carbon trading, providing robust support for the security and privacy protection of intelligent lighting systems.
Mohamed Hasan Omar, Islam Taj-Eddin, Nagwa M. Omar, Hosny Ibrahim
Traffic road violations are increasing continuously in crowded and big cities, which requires an automatic system for monitoring and detecting. This system should be accurate and secure against poisoning attacks that intend to delete some or all traffic violations. Accordingly, this study proposes a secure road traffic management system using the Internet of Things (IoT) and blockchain technology. The system uses a network of sensors, traffic signals, and cameras to track violating vehicles and record their violations in the blockchain. In the proposed system, we used deep learning models to recognize the vehicle identification number and type of traffic violations, and we stored the traffic violation data on the Ethereum test network. The proposed system consists of three steps: (1) vehicle information detection. (2) Type of violation detection. (3) Violation storage in the blockchain. The proposed secure road traffic management system uses blockchain, IoT, encryption, and authentication to increase violation detection and recording, processing speed, and communication delay, ultimately improving service quality and customer experience. The experimental results show that the proposed system is more accurate and secure than the other systems that have been proposed in recent research.