Guzalxon Belalova, Shakhida Gaybullaevna Mannanova, Botirjon Karimov
Over the past few decades, recurrent neural networks, particularly the Long Short-Term Memory (LSTM) architecture, have undergone several refinements. These networks have emerged as the go-to models for numerous machine learning challenges, especially those involving sequential data. One such application is the prediction of Bitcoin prices, a cryptocurrency that stands at the forefront of blockchain technology. This paper delves into the intricacies of forecasting Bitcoin prices using a suite of models, with a keen emphasis on the LSTM architecture, renowned for its prowess in handling tasks with long-term dependencies. Our exploration encompasses traditional time series models like ARIMA, neural network variants such as ANN and Transformer-based models, and even hybrid combinations. Specifically, our LSTM model, augmented with peephole connections, demonstrates its capability to learn and predict Bitcoin price fluctuations. We source our data from the Bitcoin Price Index and aim to gauge the accuracy with which these models can predict Bitcoin's price trajectory. Furthermore, our experiments involve the deployment of an "adam"-optimized Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, revealing insights into their predictive performances.
Chibuzo Obi-Okoli, Olamide Jogunola, Bamidele Adebisi, Mohammad Ali A. Hammoudeh
The rapid growth and psudonomity inherent in blockchain technology such as in Bitcoin and Ethereum has marred its original intent to reduce dependant on centralised system, but created an avenue for illicit activities, including fraud, phishing, scams, etc. This undermines the reputation of blockchain network, giving rise to the need to identify these illicit activities within the blockchain network. This current work tackles this crucial problem by investigating and implementing six machine learning algorithms with a particular emphasis on striking a balance between accuracy, precision and recall. The novelty of the work lies in the utilising of the synthetic minority over-sampling technique to handle data imbalance. Thus, increasing the accuracy of the light gradient boosting machine classifier to 98.4%. The outcome of this work holds great potential for enhancing the security and credibility of blockchain ecosystems paving the way for a more secure and dependable digital future in the age of decentralised and trustless systems.
This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.
Non-fungible tokens (NFTs) have become an exciting technology that provides a fresh perspective on asset ownership, provenance, and value exchange. NFTs, a blockchain-based technology, are distinct and indivisible cryptographic tokens used to confirm and record the ownership of digital and physical assets in an immutable and transparent way. The fundamental block of NFT is a smart contract built on a blockchain network. This contract contains specific information about the asset it represents, such as its unique identifier, metadata, and ownership details. The information is kept private and tamper-proof due to the decentralized and distributed structure of the blockchain, boosting faith in the token’s authenticity. The NFT is gaining popularity, but it is still in the developing stage. There is a need for a comprehensive survey to guide future research and development in NFTs. Thus, this paper presents the technical components of NFTs, their features, and the minting process. Further, this survey paper describes different token standards for NFTs. It presents various applications of NFTs in healthcare, supply chain, gaming, identity verification, agriculture, intellectual property, smart cities, charity and donation, and education. The article also emphasizes the significant difficulties faced currently in implementing NFT technology from the viewpoints of ownership, governance, and property rights, as well as security, privacy, and environmental effects. This work also elucidates the future directions to overcome the challenges in adopting NFTs in various applications.
B A Usha, S Monish, Murali Manohara Hegde A S, Nikhil Kumar · 6 authors
This survey investigates the transformative potential of blockchain technology in document generation and authentication, emphasizing its decentralized and distributed architecture for secure transaction recording and verification across a network of computers. By operating on a consensus mechanism, blockchain ensures transparency and data immutability, countering manipulation through cryptographic techniques like hashing and digital signatures. The survey explores blockchain-based approaches for certificates in education, professional certifications, and legal documentation, examining essential components such as smart contracts, cryptographic hashing, and consensus mechanisms. Platforms like Ethereum and Hyperledger are scrutinized for their strengths and limitations in certificate/document management, addressing challenges in traditional systems and underscoring the potential for heightened security and reliability in document authentication.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
B Nataraj, K R Prabha, V Swetha, S Sukitha · 5 authors
Bitcoin, a decentralized digital currency, operates without the involvement of traditional financial institutions. Bitcoin transactions are conducted directly between parties, without the use of intermediaries, thanks to blockchain technology. Wallets, public keys, and private keys are necessary for the secure transactions of this cryptocurrency. Transaction can take place using Bitcoin simply going via centralized exchanges, in contrast with numerous other cryptocurrencies. This paper explores the unique features of Bitcoin and its advantages over other crypto assets. Notably, the decentralized and distributed ledger of blockchain technology ensures the secure storage of verified bitcoin transactions by network nodes. This distinguishes Bitcoin from assets that rely on centralized exchanges for verification. Recognizing the growing acceptance of Bitcoin in various transactions, including those conducted by small businesses, this research focuses on the need for accurate early prediction of Bitcoin prices. The study proposes leveraging machine learning algorithms, specifically Random Forest and Deep Learning (Long Short Term Memory), to predict the open and close values of Bitcoin. This predictive analysis aims to assist investors in making informed decisions and optimizing their Bitcoin investments. Accurate early forecast of Bitcoin prices is a need, as this research highlights, given the increasing use of Bitcoin in a variety of activities, including small company transactions. In order to forecast the open and closing prices of Bitcoin, the study suggests using machine learning techniques, notably Random Forest and Deep Learning (Long Short Term Memory). By using predictive analysis, investors may maximize their Bitcoin investments and make well-informed judgements. The study emphasizes the importance of accurate price predictions for Bitcoin investors and introduces machine learning algorithms as effective tools for achieving this goal. A comparative analysis of Random Forest and Long Short Term Memory algorithms will be conducted to evaluate their accuracy in predicting Bitcoin prices. The research aims to provide investors with valuable insights into optimizing their investment strategies based on reliable early predictions of Bitcoin values. This idea is reliable using Machine Learning which provides effective results. By Comparing the accuracy between two algorithms the prediction of bitcoin will be implemented. This can be done using Machine Learning Algorithm (Random Forest) and Deep Learning Algorithm (Long Short Term Memory). This prediction will help the bitcoin investors to identify the open and close value of the bitcoin so that the investors can invest their bitcoin in an efficient manner and get benefited.
D. Deepa, G. M. Karpura Dheepan, R. Yogitha, K. Veena · 7 authors
In today's technology-driven landscape, the ease of access to information has led to a pressing concern: the proliferation of counterfeit documents. Document authentication plays a pivotal role in our daily lives, where proving our identity is crucial. Various identification documents like Aadhar cards, PAN cards, driving licenses, and passports require validation by human operators. To counteract the challenge of counterfeit documents, a highly accurate automated system has been devised. This system employs gradient optimization and domain-specific factors for matching ID documents with live face images. Addressing the scarcity of samples in numerous classes, a pair of interconnected systems with shared parameters facilitates efficient classifier strength training. The decentralized framework utilizes Ethereum blockchain-based innovation, ensuring digital document security through online storage, hashing, face verification, a peer-to-peer infrastructure, and QR code confirmation.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Smart contracts are tailored software services that provide consistency and autonomy. The emergence of blockchain has powerfully facilitated the development of smart contracts but also brought dramatic challenges to their security and trustwor-thiness. Plenty of malicious traps are hidden in smart contracts, causing irreversible damage and obstructing the progress of this technology. Although researchers have gradually emphasized the identification of trap contracts, existing approaches suffer from a few concerns, viz., the shortage of an efficient detection model, the unbalanced categories of trap contracts, and the absence of a high-quality dataset with multi-trap contracts. In this paper, we propose an architecture called TrapFormer to intelligently detect trap contracts in the blockchain solely by leveraging the opcodes of smart contracts. We introduce a densely connected transformer that can segmentally extract opcode features and thus distinguish any potential traps. Furthermore, we implement an adaptive data augmentation method to alleviate the category imbalance of trap contracts. To demonstrate the feasibility of the proposed solution, we construct a multi-trap contract dataset from Ethereum. The experimental results reveal that the proposed solution can achieve superior performance for practical use.
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Swetha K B Swetha K B, Preethi N. R, Prof. Veena Dhavalgi
In India with the population of 1.39 billion a Unique Identification i.e. AADHAAR Identification is a major project. This ID is common for personal and Business usage. In 2009 Government of India by Ministry of Electronic & Information Technology established UIDAI(Unique Identification Authority of India). An Integrated approach to secure Aadhaar Identity using Block chain Technology and Convolution Neural Networks. Model is being proposed using Distributed Ledger Technology(DLT) of Block chain Technology(BT) comprised of 3 phases, In first phase Biometric data and Demographic data of AADHAR is used and data reduction is done. In second phase Convolution Neural Networks( CNN)of Deep Learning with ReLU model to secure biometric data from data cloning and face verification. In 3rd phase Block chain Technology(BT) using Distributed Ledger Technology( DLT) is added to have more security to the proposed model. Thus the Security in Aadhaar Identity can be achieved.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Land administration system is important for the management, allocation, and handling related to land, affecting a wide range of stakeholders. However, one of the biggest challenges in land administration systems is maintaining the accuracy of the data stored within them. Inaccuracies can arise from a variety of factors such as errors in data collection, processing, and misuse. These inaccuracies can lead to issues such as data tampering, lengthy registration times for transactions, and the potential for double-spending, all of which can undermine the integrity of the land administration system. To address these challenges, this research paper proposes the use of blockchain technology, specifically the blockchains contract written in the Solidity programming language. The smart contract is written in the main logic of the implementation. These contracts are logical implementations to determine the owner, seller, and the transfer of the land. The proposed solution uses a distributed ledger system to ensure the accuracy of data in the land administration system. The system verifies the user's record through government officials and assigns a unique hash value to each submitted block of data. When a transaction for the transfer of ownership is initiated, a separate block is created with a unique hash value that is connected to the previous value, creating a sequence of blocks that are chained together. The hashing algorithm employed generates a fixed-size message digest, where each hashed value uniquely represents a complete set of transactions contained in a specific block.
An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential. To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value. After discussing the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain. As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies. Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.
This study aims to analyze the effect of Bitcoin price spillover volatility on Altcoin prices (Ethereum, Tether, Binance Coin) and the price of the S&P 500 Index. The data used is weekly data with a research period from January 2018 to December 2022. The analysis used in this study is the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model. The results show a volatility spillover effect between Bitcoin and Binance Coin with more positive shocks than adverse shocks in Bitcoin price volatility on Binance Coin price. Meanwhile, the spillover volatility between Bitcoin and Ethereum, Tether, and the S&P 500 Index cannot be known because the price data is homoscedastic, so it cannot be continued with EGARCH modelling because the data needs to meet the modelling requirements.
M. Vani Pujitha, Karan Kumar, D. Igna Sree, P. Yamini Devi
Particularly since Bitcoin's value and market capitalization have skyrocketed in recent years, the cryptocurrency market has attracted a lot of attention. For investors and traders, it is difficult to make wise selections due to the volatility and unpredictability of Bitcoin values. This project's goal is to forecast Bitcoin's price for the following 30 days. This prediction problem is particularly difficult due to the extreme price volatility of Bitcoin, the lack of conventional financial indications, and the complexity of the market. The price of Bitcoin for the following 30 days is predicted using a Long Short-Term Memory (LSTM) neural network in this research. The model is trained using a dataset of historical Bitcoin prices and their corresponding features such as trading volume and market capitalization. The LSTM model is trained to capture the temporal dependencies and patterns in the data, which allows it to make predictions. Previous studies have used various machine learning algorithms to predict Bitcoin prices, including ARIMA, SVM, and Random Forest. However, LSTM has shown superior performance in capturing the temporal dependencies in sequential data. The proposed LSTM model was evaluated using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), and the results showed that the LSTM model outperforms the previous studies' models in terms of accuracy. The proposed model's predictions can assist investors and traders in making informed decisions about buying or selling Bitcoin in the future.
Abstract: Certificates help students not only to prove their achieved goals and milestones but also ensure that he/she maintains a high level of knowledge in that particular field. An estimated total of 25.57 crore Indian students have been enrolled for primary to higher education in 2020–21 and nearly 65 lakhs of them graduate each year. Throughout this journey, a student generates a myriad number of certificates that may include results, transcripts, degrees, diplomas, etc. A student has to submit these certificates to apply for a job or seek higher admission in any particular organization. A major problem today is manually verifying and authenticating these certificates. Many hardworking people with genuine certificates get rejected and suffer because of the lack of a system that can differentiate original certificates from fake ones. With easy access to cheap and advanced software, document forgery has become a matter of concern nowadays. This scenario demands an updated system that could not only store documents safely but also help verify and authenticate them, their issuers, and holders in a way that is much simpler, effective, and secure. Blockchain technology comes up as a solution to all these problems. Blockchain has recently emerged as a potential means for the document-authentication process and can be easily used to tackle document forgery and counterfeiting as it follows a decentralized approach. Our proposed model includes several methods such as unique hash generation, key cryptography, digital ledgers, proof of work, digital signatures, and distributed storage which has made the document-verification process easier and more secure for both the certificate-generating organization and the holder of the certificate. The SHA-256 algorithm has been used to assign a unique hash to each uploaded document which can be used to validate its authenticity. Thus, this system meets up all the criteria for a document verification system by overcoming the drawbacks and difficulties currently faced in the traditional methods of document verification.
Blocakchain is now one of the most secure options for data protection. The rapid advancement of digital technology has brought forth new challenges in terms of data security. Organizations must adopt reliable authentication and key vaulting mechanisms to secure their data. It is advantageous to construct a secure data network due to the distributed ledger system's high level of security. Consumer goods and service providers secure and store customer data using blockchain technology. With the help of Blockchain, one of the most significant technological advancements of this century, we Although blockchain technology has high levels of security, there are still some difficulties that need to be resolved. To, solve this issue we proposed homomorphic blockchain Algorithm Secure money transactions. It can maintain our competitiveness without relying on the confidence or trust of other parties.More chances to compete with market services and consumer goods emerge as technology develops.With the development of various facets of the global economy, this technology will be employed more widely.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Nov 1, 2023·2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
Alex David S, Almas Begum, Carmel Mary Belinda M J, D Hemalatha · 5 authors
Technology and finance have experienced a change which leads to the rise of cryptocurrencies, with Bitcoin serving as a pioneer. Investors, researchers, and fans all share a fascination with bitcoin because of its decentralized structure and cryptographic security. The price volatility of Bitcoin has attracted attention and presents opportunities as well as difficulties for traders and analysts. For navigating this turbulent market, precise price prediction models are essential. In order to anticipate Bitcoin prices, this work compares Long Short-Term Memory (LSTM) with Feedforward Neural Networks (FFN). The work assesses the prediction ability of these two neural network architectures using historical pricing data and maybe other relevant factors. The comparison covers issues with anticipating Bitcoin price movements' accuracy, resilience, and generalizability.
Artificial Intelligence has largely occupied various sectors in the world. A huge number of business companies have incorporated several machine learning algorithms for day-to-day decision making. With increasing applications of AI algorithms, the concerns regarding its outcomes have also increased due to bias. In AI algorithms, bias occurs due to multiple reasons including incomplete data, skewed data, human error and so on. These algorithms have the tendency to amplify partially and discrimination in the results instead of benefiting them. This makes it compulsory for the algorithms to be audited. Currently, AI algorithm auditing processes have several challenges including tendency of biases to be deeply ingrained into the system, making these difficult to mitigate; lack of transparency in decision making and many more. This study presents the emerging technology of blockchains to be a viable solution to the existing problem. It comprehensively discusses the suitability of blockchains for transparency in the process of algorithm auditing which is bound to easily capture the issue and the layer consisting it. Consequently, the process of algorithm auditing will be more convenient and more productive. Moreover, this review also discusses some potential challenges that need to be addressed and some future recommendations for this integration.
Coin mixing is a technique used to enhance Bitcoin’s anonymity and can be used to obfuscate the relationship among transaction input addresses. Due to this property, much of the criminal activity on Bitcoin uses coin-mixing techniques to launder money, making these illicit funds difficult to trace. Therefore, it is important to implement the detection of Bitcoin mixing services. Several methods for identifying bitcoin mixing services have been proposed, but balancing their efficiency and generality at the same time is a challenging task. In this paper, We propose STMD (Spatio-Temporal Mixing Detector), which combines local features and global features of Bitcoin transactions to identify coin-mixing transactions. On one hand, we extract and process the statistical features of neighboring nodes of the transaction as local features. On the other hand, we construct a global position encoding (GPE) containing spatio-temporal information of the transaction as global features. Additionally, we employ the attention mechanism to handle these two types of features, effectively combining them. Finally, we utilize linear layers to achieve the detection of coin-mixing transactions. The experimental results show that STMD performs better than existing methods on the same dataset; it also has a higher recall on the test set of other types of coin-mixing transactions, which reflects the generality of the model. In particular, We apply local features and global features for experiments separately and verify the necessity of the two features. The results of the model trained using only global features also outperform the existing methods, which shows that the global position encoding (GPE) we constructed is effective for mixed currency transaction identification.
Hafiz Burhan Ul Haq, Minahil Irfan, Muhammad Saqlain
For the creation of cryptocurrencies like bitcoin, blockchain is the fundamental technology. Since the development of the steam engine, electricity, and computer technology, there has been a fourth industrial revolution. Blockchain technology is one of the components of this revolution, and it has been used in many sectors, including commerce, banking, and the legal system. In the beginning of this study, we talk about blocks and their many sorts. Following that, cutting-edge blockchain technology applications were covered. In addition, the benefits and drawbacks are also emphasized to help explain the blockchain idea. But there is also discussion on the use of blockchain in 5G.
Mohammad Azhar Nasirudin, Ezmin Abdullah, Khairul Khaizi Mohd Shariff, Megat Syahirul Amin Megat Ali · 6 authors
Technological advancement brings both opportunities and challenges to several sectors, including transportation. In Malaysia, the car grant system managed by the Road Transport Department (JPJ) is crucial for accurate vehicle registration and ownership but is vulnerable to fraud, like car grant forgery and unauthorized transfers. This study suggests using the Ethereum blockchain to enhance the integrity of the car grant system within the JPJ to curb these fraudulent activities. Ethereum’s decentralized and distributed ledger technology, which offers transparency, immutability, and security, is perfect for this purpose. The suggested system uses smart contracts, self-executing agreements with preset rules, to ensure secure car grant registration and transfers. These contracts execute operations securely, preventing unauthorized changes. A prototype was developed and tested to evaluate the feasibility and efficacy of the system, including transaction speed, security, scalability, and user experience. The results gave valuable insights into the system’s strengths and weaknesses, allowing for further optimization. This secure and transparent method could revolutionize vehicle registration and ownership procedures, enhancing trust, efficiency, and accountability in the Malaysian transportation sector.