Illegal use of electricity is very common in cryptocurrency mining farms, as energy bills are the most important component of the cost of cryptocurrency production. In this case, it raises the issue of how to detect illegal cryptocurrency mining. Innovative approaches are needed to identify data centers that illegally mine cryptocurrencies. This study proposes the use of unique noise and/or harmonic features of cryptocurrency generating machines to detect illegal cryptocurrency mining farms. Within the scope of this study, the characteristic harmonics originating from the data centers were determined by performing field tests on the neutral line of the electrical grid. In this study, it has been shown that electricity distribution companies can detect illegal cryptocurrency data centers using potential illegal electricity by monitoring energy quality data. Legal permissions can be obtained easily for detailed examination and detection in cryptocurrency data centers of using illegal electricity. With the proposed innovative approach, the time taken to detect illegal cryptocurrency mining farms using illegal electricity is reduced.
Felipe Benedet da Silva, Diego Fernandes Gonçalves Martins, Marco Aurélio Amaral Henriques
A blockchain é um sistema distribuído utilizado para registro de transações financeiras e de outras informações confidenciais, seguindo um mecanismo de consenso específico para garantir sua segurança. Atualmente, o Proof-of-Work é o consenso mais utilizado, mas tem limitações relacionadas a sua eficiência e consumo de energia elétrica, aumentando o interesse em outros mecanismos, como o Proof-of-Stake. Nosso grupo de pesquisa propôs anteriormente um novo consenso Proof-of-Stake que não requer comitês de validação, sendo assim mais eficiente que outros consensos. O propósito deste artigo é propor novos métodos para avaliar o novo mecanismo de consenso em cenários reais, considerando uma forte criação e disseminação de transações entre todos os nós. A avaliação também considera a criação e transmissão de blocos completamente carregados de transações, tornando a atividade da rede mais intensa e próxima de uma aplicação sob uma carga severa de trabalho.
Power is considered as a boon for life, lifeline of a country's economy; a key instrument for the socio-economic evolution of any country. Pakistan faces the issue of power theft that worsens the economy of utilities. The purpose of research is to modernize Pakistan's power sector with the usage of Blockchain. Research is based on a comparative analysis of proof of stake (POS) and proof of authority (POA) consensus protocols for the power theft issue for small, medium, or large scales of consumers; results showing automated and pre-selected processing time of transactions running on different nodes of POS and POA along. By comparative analysis of POS and POA; it is shown that the performance of Blockchain remains unaffected by the consensus protocol. To further strengthen the findings the statistical analysis data is carried out to establish the uniqueness of sampled data. The result establishes that Blockchain performs equally well for different architectures of the proposed architecture. The consensus protocol helps in achieving a suitable governing style with no effect on Blockchain performance.
Abdullah Musaed Alkhiari, Shailendra Mishra, Mohammed Alshehri
Smart Grid is a power grid that improves flexibility, reliability, and efficiency through smart meters. Due to extensive data exchange over the Internet, the smart grid faces many security challenges that have led to data loss, data compromise, and high power consumption. Moreover, the lack of hardware protection and physical attacks reduce the overall performance of the smart grid network. We proposed the BLIDSE model (Blockchain-based secure quantum key distribution and Intrusion Detection System in Edge Enables Smart Grid Network) to address these issues. The proposed model includes five phases: The first phase is blockchain-based secure user authentication, where all smart meters are first registered in the blockchain, and then the blockchain generates a secret key. The blockchain verifies the user ID and the secret key during authentication matches the one authorized to access the network. The secret key is shared during transmission through secure quantum key distribution (SQKD). The second phase is the lightweight data encryption, for which we use a lightweight symmetric encryption algorithm, named Camellia. The third phase is the multi-constraint-based edge selection; the data are transmitted to the control center through the edge server, which is also authenticated by blockchain to enhance the security during the data transmission. We proposed a perfect matching algorithm for selecting the optimal edge. The fourth phase is a dual intrusion detection system which acts as a firewall used to drop irrelevant packets, and data packets are classified into normal, physical errors and attacks, which is done by Double Deep Q Network (DDQN). The last phase is optimal user privacy management. In this phase, smart meter updates and revocations are done, for which we proposed Forensic based Investigation Optimization (FBI), which improves the security of the smart grid network. The simulation is performed using network simulator NS3.26, which evaluates the performance in terms of computational complexity, accuracy, false detection, and false alarm rate. The proposed BLIDSE model effectively mitigates cyber-attacks, thereby contributing to improved security in the network.
This paper is a review of some real-time issues associated with the development of Ethereum smart contracts like out of gas exception and gas inefficient code patterns and focuses on the methods and solutions presented in recent years. With the help of this paper, we are trying to summarize the methods which can be used in future researches for gas prediction with the help of old transaction data and machine learning algorithms and have a look at old researches which are trying to predict with the help of regression algorithms and their efficiency. KEYWORDS: Blockchain, Etherium, Gas Consumption, Gas Prediction, Transaction
Musse Mohamud Ahmed, Mohammad Kamrul Hasan, Muhammad Shafiq, Md Ohirul Qays · 7 authors
Utilities produce and supply products following local requirements and with the synchronizations which connect subscribers. Harmonics is a power efficiency/quality variable caused by electronic devices that domestic and industrial consumers use. The famous IEEE Standard 519 is maintained to calculate harmonic limits, which ensures power efficiency. In a standard power system, currents and voltages generate pure sine wave signals during regular operations. As harmonics influence the power system, they cause interference in the sine wave signals. So, the best practice method should be used to resolve the harmonics issue. One of the problem-solving techniques of harmonics is the measurement and reduction of harmonics detection, and it uses Fast Fourier Transform (FFT). Therefore, power output should assess in a peer-to-peer Blockchain scheme by measuring and minimizing harmonics detection. This paper uses a Shunt Active Power Filter (SHAPF). It describes the simulation analysis and reduction of harmonics detection in a peer-to-peer interconnected 3-phase power system with the help of an FFT algorithm. This research was carried out to assess the efficiency of the AC signal by collecting, processing, and evaluating power data. Using the shunt active filter, the proposed design outperformed the traditional methods for both six and twelve pulse rectifiers, achieving total harmonic distortion (THD) of only 1.42% and 0.92%, respectively.
Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.
Power system reforms worldwide have commoditized electric energy, and thus the electricity market has been developed. With this, trading of electric energy takes place in various time-domains like the day ahead, real-time, etc. These transactions take place over the counter (OTC) or Power Exchange (Px), which provide the market participants with the required platform and payment security. The transactions on OTC and Px requires a third-party platform and guarantee for contract and settlement, there incurs overhead cost. Since electric energy is a fungible commodity, it can be transacted very well with the old system like barter. Energy Banking is one such mechanism wherein one utility supplies the energy to another utility that needs it more and, in leisure, the energy can then be provided back. The requisite security of the transactions can be provided by blockchain technology. Energy banking is presently being done only on a MW quantum basis with no price tag, despite the cost being dependent on the demand-supply ratio. To ensure energy banking transactions in real-time and free from the perils of financial settlements, this article suggests the use of the Peer-to-Peer (P2P) model of blockchain technology for executing Smart Contracts mutually agreed upon by both parties and avoiding third party overhead costs. Doi: 10.28991/HIJ-2021-02-03-03 Full Text: PDF
Blockchain commoditizes confidence in a step to grid flexibility and allows decentralized smart contracts to enforce auditable multiparty payments based on established standards among distributed power suppliers and customers. Smart contracts reduce the need to communicate with foreign entities, making it feasible for distributed energy trades and exchanges, including energy flows and financial activity, to be implemented and monetized. It might help decrease the cost of transactive energy and increase protection. This work provides how a renewable energy business is secure and how privacy and anonymity are given for such a system through blockchain implementation.
Rohan Kumar C L, Ali M. Zain, Ali M. Zain, A V Prajwal · 5 authors
Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.
Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.
Diogo M. F. Mattos, Dianne S. V. Medeiros, Diego Passos, Natália C. Fernandes · 7 authors
The electric power grid is the world's largest engineering system, and its secure and reliable operation is vital to human activities. The introduction of intelligence in the electrical power grid through smart grids imposes challenges that require new techniques and approaches to provide cyber-physical security. In this article, we discuss the use of blockchain to provide security and reliability to smart grids. Blockchain allows untrusted nodes to correctly and verifiably interact with each other in a distributed peer-to-peer network, without any reliable intermediary. We explore smart contracts, codes resident in blockchain that automate multi-step processes, as a way to automatically trade electric energy. We also discuss initiatives, challenges, and research opportunities of blockchain technologies in the electrical sector.
Juan Carlos Olivares, Enrique Reyes‐Archundia, José Antonio Gutiérrez Gnecchi, Ismael Molina‐Moreno · 6 authors
The lowering cost of electricity generation technologies by renewable means has caused the role of end-users to change from being a simple consumer to a producer of their energy. This new role has been called a prosumer and has contributed to modifying electricity markets, by allowing prosumers to merchandize the energy surplus to electricity companies and other prosumers. The fundamental key to any economic transaction is trust between the parties. Recently, new technologies, such as blockchain, are transforming transaction security, and allowing decentralization of the smart power grid. This study presents the development of a transactional model of electrical energy using the infrastructure of smart metering and blockchain systems to allow the commercialization of electrical energy by prosumers. The results show that for the Mexican electricity market, they can be good alternatives by reducing subsidies and being more profitable than current rates.
Bitcoin is one of the primary computerized monetary forms to utilize peer innovation to work with moment installments. The free people and organizations who own the overseeing figuring control and take part in the bitcoin network—bitcoin miners— are accountable for preparing the exchanges on the blockchain and are persuaded by remunerations (the arrival of new bitcoin) and exchange charges paid in bitcoin. These excavators can be considered as the decentralized authority implementing the believability of the bitcoin network. New bitcoin is delivered to the excavators at a fixed yet occasionally declining rate. There is just 21 million bitcoin that can be mine altogether. As of January 30, 2021, there are around 18,614,806 bitcoin in presence and 2,385,193 bitcoin left to be mined. This paper will predict the nature of bitcoin price because, according to the reports of the past few years. The year 2020-present appeared to be a good time for bitcoin because, in this time duration, bitcoin has seen huge ups and downs. This paper will use various Machine Learning Techniques for the predictive analysis of bitcoin to accurately predict the price's nature. As the price of bitcoin depends upon various factors and these factors directly affect the price, i.e., multiple factors of bitcoin are dependent on each other. After analyzing the results from multiple research papers and review papers, we discovered each algorithm has its advantages and disadvantages while predicting the bitcoin value. Keeping in mind all the findings, we will find algorithms that predict the bitcoin price accurately and without fewer disadvantages. So, if we go as per assumptions, regression would be the best choice for predicting the bitcoin value, but there are others algorithms also. So, in this paper, we will see the results of the multiple algorithms and then choose the correct algorithm after analyzing the results of all the implemented algorithms. This paper also includes the implementation of the comparison charts with each algorithm so that it will be easy to analyze the findings of each algorithm.
A rede Bitcoin é um sucesso por permitir a transferência de criptomoeda com um baixo custo, de forma rápida, sem limites geográficos e sem a intervenção de um banco intermediador. Está sendo apontada como uma possível solução para mais de um bilhão de pessoas que não tem acesso ao sistema financeiro por causa dos altos custos. Por outro lado, a rede Biticoin é pseudo-anônima e tem sido usada para uma enorme variedade de atividades financeiras dúbias e ilegais. Este artigo investiga as atividades de lavagem de dinheiro na rede Bitcoin através de diversos mecanismos que procuram melhorar o desempenho de classificadores na análise de um conjunto de dados desbalanceado devido a uma classe minoritária com muito poucas amostras. A análise considera o conjunto de dados Elliptic com mais de 200 mil transações de Bitcoin, sendo o maior conjunto rotulado de dados publicamente disponível que existe hoje de todas as criptomoedas. Os experimentos realizados mostram a eficácia de cada estratégia na melhora da classificação das atividades de lavagem de dinheiro tais como: i) o percentual de repartição do conjunto de dados em treino e teste; ii) heurísticas de sobre-amostragem; ii) diferentes algoritmos de aprendizado de máquina; iv) algoritmo de reforço de aprendizado adaptativo e v) descoberta automática de características. Os resultados mostram um bom desempenho do algoritmo de sobre-amostragem AdaSyn e que o maior ganho em desempenho foi com o classificador floresta aleatória.
The next-generation energy system, i.e., Smart Grid (SG), empowers the real-time transfer of information using advanced metering infrastructure (AMI) and smart meter (SM) between end-consumers and grid. It accelerates various services such as automatic meter reading, time-of-use (TOU) pricing, demand-response management, and many more. Though it has growing security and privacy concerns and the detection of malicious activity is a critical security task that sacrifices the overall Quality-of-Service (QoS) of SG and Quality-of-Experience (QoE) for customers. To address the aforementioned issues, we propose a data analytics Scheme ArMor for malicious activity detection on the blockchain (BC)-based SG system. The ArMor detects data integrity issues in real-time like false data injection attack and SM failure. Here, we proposed a unique ARIMA-based malicious activity detection model and classified the customer. Then, we proposed a Smart Contract (SC)-based incentive mechanism for utility providers handling the malicious activity at their end. It prevents the entry of malicious data into the SG system as transactional data once stored in BC, it is secured using SC. The obtained results are compared against parameters like prediction accuracy, latency, and data storage cost compared to the state-of-the-art approaches to designate the efficacy of the proposed scheme.
Aasim Ullah, S.M. Shahnewaz Siddiquee, Md Akbar Hossain, Sayan Kumar Ray
Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.
Energy supply industries play a vital role in a country. Inefficiencies in the energy supply chain regarding tricky contest and the lack of management instantly change energy tariff calculation. This work proposes the Ethereum blockchain platform with existing traditional infrastructure to track and investigate energy supply chain activities using a unique identity with smart contracts. It maintains the records of the organization's activities that are protected and available to stakeholders according to the recognized collection of procedures and practices without requiring any centralized administration. This paper focuses entirely on analyzing and developing a simplified, low-cost economical solution to quickly connect the present energy supply industry at various geological locations to track and trace the linked data in the energy market.
The power grid is a critical infrastructure of a country that needs protection and security. According to the report of the International Energy Agency, the electricity demand is constantly increasing the world over. Countries are moving towards green energy and efforts are being made to integrate these green energy into the main grid. Smart Grid will improve the reliability and efficiency of the grid by managing the energy demand. Cyber-attacks and cyber terrorism is also increasingly targeting the electrical grid. Intruders may try to gain access to the grid by exploiting the vulnerability of the grid. IEDs/devices are the endpoints of the network and they are the weakest link in the entire network. Endpoints must be protected by providing better identification and authentication. Blockchain uses a cryptographic hash function and digital signature to identify a device in a network that is arranged as a chain of the blocks. This research illustrates the methodology to be adopted for implementing Blockchain in Smart Grid for device identification and authentication. Implementation of blockchain was done on an experimental basis and satisfactory results are obtained. The security of the grid will be greatly enhanced with blockchain-based implementation.