Mubarak A. Khan, Md Mofijul Islam, Istiaq Ahmed, Md Masud Karim Rabbi · 10 authors
Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets. To guarantee the reliability and accuracy of the used machine learning models, an extensive battery of evaluation metrics was utilized. The plot demonstrates clearly that XG-Boost obtained the highest accuracy out of the three models, Random Forest was slightly lower, and conversely, Logistic Regression was the lowest of the three models. Integrating blockchain technology with AI can increase the transparency, security, and efficiency of the energy sector in the U.S. Blockchain's decentralized and immutable ledger can make energy transactions traceable and resistant to tampering, and it becomes extremely hard for malicious actors to manipulate prices or fake records. In the future, the integration of deep learning methodologies and real-time integration of data from the Internet of Things (IoT) holds promising implications for future improvements. Deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can detect strongly nonlinear patterns of fraud, which conventional models may not identify, particularly for the usage of multivariate time-series data from smart meters, sensors, and distributed energy resources.
With the rapid growth of digital multimedia content and sophisticated adversary technologies, ensuring authenticity and security has become a critical challenge. Traditional methods,such as digital watermarks and cryptographic signatures, help verify authenticity. However, they come with various limitations, including challenges in key management, reliance on central authorities, and the implicit trust placed in these authorities, which can undermine security and authenticity. To address these challenges, this article presents a Blockchain (BC)-based approach using Ethereum for the authentication of multimedia content. Using Ethereum’s secure and transparent ledger, we create an immutable record of ownership and modifications, ensuring that digital content remains tamperproof and verifiable. Our analysis shows that the use of Ethereum Smart Contracts (ESC) improves the reliability of digital content authentication, making it more secure and decentralized. Experiments reveal that our executed ESC consumes 0.0011 SepoliaETH to store the metadata in BC and zero gas for authentication verification. This research highlights how EBC can provide a robust and transparent solution to protect multimedia content, ensuring its integrity and authenticity in a trustless environment.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Bitcoin is the most traded cryptocurrency by volume and market cap. A number of scholars have directed their research towards characterizing Bitcoin’s speculative behavior using a myriad of techniques such as technical analysis, price regression, and direction classification. For this work, research is conducted using the relatively nascent technique of on-chain data analysis. The goal of this research is to evaluate Bitcoin’s on-chain data in predicting future price direction. First, a classification process of on-chain data features that helps the reader understand their relevance is proposed. To address the curse of dimensionality, feature selection algorithms such as L1 regression, Boruta, and the dimensionality reduction algorithm Principal Component Analysis (PCA) are utilized. The research then explores advanced neural networks for next day price direction prediction, including the Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the Temporal Convolutional Network (TCN). Neural network models and trading strategies are then compared based on their return statistics. A comparative analysis of feature selection, learning model performance, and trading strategy performance is also conducted. Results from the research show that the Boruta feature selection algorithm combined with the CNN-LSTM model performs best compared to other combinations with a prediction accuracy of 82.03% over the testing period. In addition, the on-chain features within the category, realized value, and unrealized value classifications have higher predictive powers for next day price direction prediction. Finally, during trade simulations, the CNN-LSTM model with a Long-Short strategy had an annualized return of 1682.7% and a Sharpe Ratio of 6.47.
The rise of cryptocurrency and asset digitalization has created new opportunities for fraud, cyberattack, and financial crime. Conventional security mechanisms are not able to address dynamic threats, and hence the need of adopting advanced technologies to strengthen transaction security and fraud prevention. The synergy of AI and Blockchain Technology offers a revolutionary solution, blending decentralized security paradigms with cognitive fraud detection entities. As its transaction record is not owned by a single entity, it influences transparency and security of the transaction making it a robust candidate to pursue sustainable transition in PH. Smart contracts facilitate automated transaction validation and compliance enforcement mechanisms by leveraging predefined rules, resulting in reduced human intervention potential fraud. ML & DL-based AI fraud detection models monitor transaction pattern, such as irregularities, suspicious behavior, and potential threats, detector in real-time using the raw data. Fraud prevention mechanisms are enhanced by efficiently accurate and effective its capabilities using techniques such as anomaly detection, behavioral analytics, and predictive modeling. The risk assessment models driven by AI also help validate individuals and comply with anti-money laundering (AML) policies, thereby enhancing the security of finances. However, despite these improvement’s strides, there remain challenges that must be tackled for mass adoption such as scalability, regulatory compliance, data security and computational complexity. The future of financial transactions with blockchains lies in security, privacy, and efficiency, and future work should examine the combination of federated learning, homomorphic encryption, and quantum-resistant blockchain algorithms. AI and blockchain work together to protect digital assets, keep financial transparency, and reduce fraud risks in crypto and finance networks.
G. Jagadeeshwar, G. Sri Ram, P. Anunay, M. A. Jabbar
Counterfeit products are creating a huge problem in global markets, causing huge financial losses to companies and damaging consumer trust. Traditional ways of checking product authenticity are failing to keep up with modern counterfeiting techniques. To solve this, we proposed a blockchain-based counterfeit product detection system using the Algorand blockchain. Upon registration in the system, each product gets a unique Quick Response (QR) code containing its details, which is then linked to a secure digital record on the Algorand blockchain. When scanned by the consumer, the system checks the details of the product stored on the blockchain along with the data stored off-chain and verifies whether it is a counterfeit or not. Using Algo-rand’s Pure Proof-of-Stake (PPoS) consensus, the system ensures fast, low-cost, and reliable verification without the requirement of a central authority. This approach improves security, reduces fraud, and allows customers to verify products easily. While some blockchain-based authentication systems include manufacturer registration and additional security measures, this paper mainly focuses on registering and verifying products.
Counterfeiting in footwear a very big issue in the industry. This is a prevalent problem in sneakers due to the popularity and rarity of these footwear, this practice undermines consumer trust and brand reputation. In response, this blockchain-based sneaker authentication system proposes to solve this problem by using technologies such as decentralization and QR Codes embedded in sneakers to ensure that each step of the process from manufacturer to consumer is transparent and open. Manufacturers use the Ethereum blockchain to add sellers and their product. Each serial number of their products are mapped to a seller which then, using this secure platform can make a sale and map each pair of shoes to a consumer. This project utilizes advanced authentication algorithms, signature validation and Solidity contracts to enhance the system&s;s integrity. This proof of concept aims to instill confidence among consumers and the stakeholders (sellers, manufacturers) by identifying counterfeit products.
Cryptocurrency is a new type of asset that has emerged with the advancement of financial technology, creating significant opportunities for research. bitcoin is the most valuable cryptocurrency and holds significant research value. However, due to the significant fluctuations in bitcoin's value in recent years, predicting its value and ensuring the reliability of these predictions, which have become crucial, have gained increasing importance. A method that combines Long Short-term Memory (LSTM) with conformal prediction is proposed in this paper. Initially, the high-dimensional features in the dataset are divided using the Spearman correlation coefficient method, and features below 0.75 and above 0.95 are excluded. Subsequently, an LSTM model is built, and data are fed into it and the data is used to train the model to generate predictions. Finally, the predicted values generated by the LSTM are fed into the conformal prediction model, and confidence intervals for these values are generated to verify their reliability. In the conformal prediction model, the quantile loss of the loss function is defined, and an Average Coverage Interval (ACI) predictor is designed to improve the accuracy of the results. The experiments are conducted using data from CoinGecko, which is a publicly available data. The results show that the LSTM-conformal prediction (LSTM-CP) combination improves reliability.
Dušan Morháč, Kristián Košťál, Viktor Valaštín, Ivan Kotuliak
Abstract Interoperability is a critical aspect of fluent liquidity and healthy blockchain ecosystems. Achieving seamless interoperability within heterogeneous blockchains often proves challenging due to the inconsistencies in architecture, asset registration and management logic, implementations, and smart contract designs. These unresolved challenges frequently cause fragmented liquidity within ecosystems. This paper proposes a modular and novel solution, UniSpell, that works as a universal adapter to achieve seamless interoperability and overcome fragmented liquidity problems. This is possible by leveraging Polkadot’s native cross-chain protocol, XCMP, allowing developers to implement dApps that can source liquidity, cross-chain transfer, and swap assets within one line of code. UniSpell presents a framework that is easily extendable and applicable to multichain ecosystems outside of its native ecosystem, Polkadot. UniSpell’s primary goal is to cultivate a dynamic ecosystem by enhancing liquidity and encouraging the addition of new assets. This approach enables innovation and broadens the adoption of decentralized finance (DeFi) solutions.
Ali Al Maqousi, Ammar Almomani, Ahmad Al–Qerem, Mouhammd Alkasassbeh
Blockchain has emerged as a distributed ledger mechanism that enables decentralized recordkeeping and transaction validation with applications in various fields. Recent research has highlighted its potential for managing urban water and energy systems, especially given increasing urbanization and climate-related resource pressures. This chapter explores theoretical dimensions of blockchain for integrated urban water and energy management. It examines the conceptual linkages of blockchain-based smart contracts, distributed consensus, and tamper-proof data exchange with the operational and strategic needs of urban water and energy stakeholders. It discusses the roles of trust, security, and data transparency in facilitating stakeholder cooperation in resource allocation and highlights resilience benefits for water supply networks and energy grids.
Abdullah Ayub Khan, Asif Ali Laghari, Syed Azeem Inam, Sajid Ullah · 6 authors
Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Blockchain has evolved from supporting cryptocurrencies to transforming industries like finance, healthcare, and supply chain management. Emerging trends focus on scalability with Layer 2 solutions, sharding, and cross-chain interoperability. Sustainability efforts include transitioning to Proof-of-Stake, carbon-neutral blockchains, and renewable energy. AI integration enables decentralized models, secure data sharing, and AI-driven smart contracts. Governments explore CBDCs, while privacy technologies like Zero-Knowledge Proofs enhance security. Challenges remain in regulation, security risks, and adoption, but ongoing innovations are driving blockchain's widespread acceptance.
Bitcoin is a decentralised digital currency that has been in existence for some time now. Its value has been volatile, with most of its prices fluctuating significantly, making it difficult to predict its prices when investing. This paper employed a Neural Basis Expansion Analysis Time Serie (N-BEATS) deep learning architecture to predict Bitcoin prices. The model was chosen because of its proven capabilities of modelling intricate patterns in time series data. An hourly Bitcoin price data of 729 days collected from Yahoo Finance extensively assesses the N-BEATS model's performance while comparing it with other machine learning models like the Linear Regression and the long-short-term-memory (LSTM) networks. Mean Absolute Error (MAE) and R-squared (R²) were utilised as performance evaluation metrics. N-BEATS surpassed the others by providing an R² score of 0.00240 and an MAE score of 0.9998. These findings are significant and shed light on how deep learning models can be used for financial forecasting. The result shows that the N-BEATS model is more accurate and reliable for predicting cryptocurrencies' prices, which may be very useful for investors in making decisions and managing risks.
This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.
Xiangyu Bai, Jiali Hu, Xianming Liu, Yaqin Deng · 6 authors
The purpose of this paper is to discuss the design and implementation of the upgrade of encryption technology of the Grand Canal digital document system based on blockchain technology. Based on the core technologies of blockchain's distributed ledger, smart contract and encryption algorithm, this study proposes a novel upgrade scheme for the Grand Canal digital document system. The scheme realizes the non-tampering and full traceability of document data by constructing a decentralized document storage architecture, and optimizes the document management process by using smart contract technology and asymmetric encryption algorithm to ensure the security of document transmission and storage. As a modern technology with strong confidentiality and security, blockchain is extremely important for data protection, and is of great practical significance for promoting the digital inheritance and innovative development of the cultural heritage of the Grand Canal.
<p>Blockchain technology is the foundation of cryptocurrencies, which are virtual currencies. The decentralized nature of cryptocurrencies has resulted in a significant reduction of central authority over them, which has implications for global trade and relations. The need for an effective model to anticipate the price of cryptocurrencies is essential due to their wide variations in value. Due to the shortcomings of conventional production forecasting, in this work, four distinct models were used. The deep learning models are the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), and both the Facebook-Prophet and Silverkite support the exponential smoothing technique. Silverkite is designed to handle a wide range of time series forecasting tasks. Considering past bitcoin information from January 2012 to March 2021, a period of nine years, we looked at the models. The Bi-LSTM model yields a 7.073 mean absolute error (MAE) and a 3.639 root mean squared error (RMSE). The Bi-LSTM model identifies the deviations that might draw attention and avert any problems.</p>
Forecasting bitcoin prices using deep learning techniques has gained significant attention recently. Despite the success of Generative Adversarial Networks (GANs) across various domains, there is a crucial issue in bitcoin price prediction: the ability to accurately capture correlations between temporal data points. Moreover, GANs face notable inherent challenges, particularly training instability and mode collapse. In this paper, we employ windowing and conditioning techniques to capture correlations between prices and other relevant features such as technical indicators. Additionally, we use a hybrid Long Short-Term Memory (LSTM)-based generator that combines a convolution layer and LSTM to improve learning from temporal data. Appropriate loss functions are also utilized for the discriminator and generator to enhance training stability and mitigate mode collapse. The proposed method was evaluated on the historical Bitcoin data sourced from the Yahoo Finance website. Experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods. The source code for the proposed method is available on GitHub: https://github.com/mahdimanian/draganbtc/
M. Kathiravan, M. Meenakshi, M Kaviya, S. Sreesubha · 6 authors
Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The “mining” process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as “nodes” or “miners.” The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly; hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods.
Ni Wayan Wardani, Putu Gede Surya Cipta Nugraha, Kadek Nonik Erawati
The volatile nature of Bitcoin poses significant challenges for accurate price prediction, which is critical for informed decision-making by investors and policymakers. This study explores the application of decision tree algorithms to predict Bitcoin prices using a dataset comprising historical data on Bitcoin prices, market capitalization, and trading volumes. The research emphasizes feature engineering techniques, including derived metrics such as rolling averages and volatility indices, and integrates ensemble methods like Random Forest and Gradient Boosting to enhance predictive performance. The decision tree model achieved an accuracy of 53%, demonstrating its capability to capture general trends in Bitcoin price movements, particularly during high volatility periods. The study highlights the importance of key features such as the Relative Strength Index (RSI) and Moving Averages (MA14) while identifying limitations in predicting price decreases. Recommendations for future research include integrating external data sources, such as sentiment analysis and macroeconomic indicators, and exploring advanced modeling techniques to improve robustness and accuracy. This research contributes to the growing field of cryptocurrency price prediction by providing interpretable and actionable insights into market dynamics. The findings offer valuable tools for analysts and investors navigating the complexities of the cryptocurrency market.
Investment is an activity that is popular nowadays. Profitable investments are the hope of every investor. By investing. investors expect the invested assets to generate returns and to obtain profits for future life In investment studies. the most frequently discussed topic is the fluctuations. whether increases or decreases. of an asset's price (stocks). The risk of investment is loss in financial. The fluctuations of stock prices represent risks in the investment field. One measure used to determine gains and losses from stock prices is the return. To know return from data. we may use the compound return formula. Returns have empirical facts that require several tests. In this study. the empirical facts of returns are that the returns are not autocorrelated (autocorrelation function) and that the returns are leptokurtic distributed (thick-tailed distribution). We use the price data of Bitcoin (BTC) and Gasoline (UGA) from January 1. 2019. to December 31. 2023. The main of purpose of this research is to show empirical analysis of the Bitcoin and Gasoline return data. The results of the empirical analysis show that the return of stock price for Bitcoin (BTC) and Gasoline (UGA) meet the empirical properties of returns so that they can capture a good volatility model.
This research explores the application of an Attention-based GRU model for predicting Bitcoin price movements. Historical data from Yahoo Finance, along with technical indicators such as the Stochastic Oscillator (KD index), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), were used to enhance prediction accuracy. The model was designed to focus on key time-series patterns, with the attention mechanism improving its ability to capture crucial market signals. Results show that incorporating these indicators improved performance, with the MACD-enhanced GRU model achieving an accuracy of 75%. The model's effectiveness in volatile cryptocurrency markets highlights the advantages of deep learning models, combined with technical indicators, for accurate financial forecasting.