Ali Yassine, Mohamed Baza, Sherif Abdelfattah, Mahmoud M. Badr · 6 authors
The smart grid was introduced as an evolved version of the traditional power grid to enable two-way communication between consumers and electricity providers. It aims to distribute energy generation points that utilize renewable energy and enable energy trading among users. However, this grid has an increasing number of components, including consumers, prosumers, generation plants, and electric vehicles. Managing them using a centralized architecture approach introduces several challenges such as single points of failure and cyberattacks. To address this issue, blockchain technology was proposed as an infrastructure to secure the smart grid. The latter provides a distributed ledger that handles transactions in a safe environment. However, despite all its advantages, blockchain has several scalability and privacy protection issues. In this paper, we highlight the state-of-the-art of blockchain-based smart grids works. The article also provides a comprehensive survey on the challenges that face blockchain-based smart grids and points out timely future research topics.
The total number of solar power-producing facilities whose Feed-in Tariff (FIT) Program-based ten-year contracts will expire by 2023 is expected to reach approximately 1.65 million in Japan. If the facilities that produce or consume renewable energy would increase to reach a large number, e.g., two million, blockchain would not be capable of processing all the transactions. In this work, we propose a blockchain-based electricity-tracking platform for renewable energy, called ‘ZGridBC,’ which consists of mutually cooperative two novel decentralized schemes to solve scalability, storage cost, and privacy issues at the same time. One is the electricity production resource management, which is an efficient data management scheme that manages electricity production resources (EPRs) on the blockchain by using UTXO tokens extended to two-dimension (period and electricity amount) to prevent double-spending. The other is the electricity-tracking proof, which is a massive data aggregation scheme that significantly reduces the amount of data managed on the blockchain by using zero-knowledge proof (ZKP). Thereafter, we illustrate the architecture of ZGridBC, consider its scalability, security, and privacy, and illustrate the implementation of ZGridBC. Finally, we evaluate the scalability of ZGridBC, which handles two million electricity facilities with far less cost per environmental value compared with the price of the environmental value proposed by METI (=0.3 yen/kWh).
A C Ramachandra, P. S. Aishwarya, Aman Sharma, Aman Verma · 5 authors
Coffee is a highly popular and widely traded beverage across the globe. Its consumption is steadily increasing at an annual rate above 2%, as per the 'Coffee development report 2019' by ICO. It has been observed that the coffee market has grown significantly by over 60% since the year 1990. In India, there are around 250,000 coffee growers, out of which 98% are small growers with farms of 10 acres or fewer. Due to limited access to official financial support, middlemen play a crucial role in providing credit, technical support, and trading services to smallholder coffee growers. However, there are risks associated with adulteration, poor storage, and transportation facilities. Moreover, climate changes have also adversely affected coffee production. Conventionally, coffee trade is conducted through middlemen via phone calls and paper documentation, which leaves room for potential fraud and data manipulation. The objective is to resolve these challenges and ensure full transparency in trading through the utilization of advanced technologies. We suggest employing a smart contract for the coffee supply chain by incorporating Blockchain and IoT devices. Blockchain is a tamper-proof digital ledger distributed across the network, containing immutable digital records. This will guarantee complete transparency of data and avoid any manipulation. Throughout this project, we gained expertise in various aspects of the supply chain and acquired technical knowledge of IoT devices and Blockchain. We designed a proposal that could be utilized for industrial purposes and promote societal development. Additionally, this smart contract can be utilized for other products besides coffee, thereby enhancing the complete supply chain of different agri-products throughout the nation.
As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.
We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and--the student surpassing the teacher--found vulnerable contracts that Slither mislabeled. DLVA is much faster than other smart contract vulnerability detectors: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a 10-1,000x speedup. DLVA has three key components. First, Smart Contract to Vector (SC2V) uses neural networks to map smart contract bytecode to a high-dimensional floating-point vector. We benchmark SC2V against 4 state-of-the-art graph neural networks and show that it improves model differentiation by 2.2%. Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has a Slither-predictive accuracy of 97.4% with a false positive rate of only 0.1%. Third, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. We benchmark DLVA's CC with 10 ML techniques and show that the CC improves accuracy by 11.3%. Overall, DLVA predicts Slither's labels with an overall accuracy of 92.7% and associated false positive rate of 7.2%. Lastly, we benchmark DLVA against nine well-known smart contract analysis tools. Despite using much less analysis time, DLVA completed every query, leading the pack with an average accuracy of 99.7%, pleasingly balancing high true positive rates with low false positive rates.
With increasing activities in cryptocurrencies and their fast-growing global mining demand comes new opportunities and challenges facing the electric energy systems. From the electric grid perspective, the key challenges are how to properly monitor and predict cryptocurrency mining demand at wholesale and retail levels. While large-scale mining companies connected to the transmission level can use directly instrument sensors to monitor their mining demand, how to monitor behind-the-meter cryptocurrency mining demand is still an open question. In this paper, we propose an edge-level distribution level Bitcoin mining detection scheme that utilizes smart meter data to detect the on/off status of the mining machines and estimates the power consumption magnitude of the mining load in each house. We investigate the performance of our algorithm with different Bitcoin load variations representing a wide range of possible mining devices and behaviors. Numerical results suggest that the proposed algorithm can detect both the on/off status of these loads with above 94% accuracy and calculate its load magnitude with less than 16% error for common ASIC miners. Building upon this method, aggregators could coordinate individual household mining loads for participation in demand response programs that help reduce peak demand and increase social welfare.
Blockchain is the fundamental component of smart contract applications. Testing technology plays a special and irreplaceable role in the development of smart contract applications in blockchain-based applications, especially in distributed renewable energy transaction scenarios in the energy industry. In practice, the formulation of blockchain technology as a standard infrastructure is an essential means to improve the reliability of blockchain-based applications in the energy industry. However, the quality of the organization-level blockchain still encounters many challenges, such as password attacks, and double spending attacks, which attract much attention from both research and academic area. Much research has focused on quality improvement through testing to fulfill the requirement toward functional, performance, and security requirements of the industry. However, the framework to accomplish the specific testing task was not present comprehensively yet. In This paper, an investigation was given on the supervision and testing of blockchain-based applications in the energy industry after its operation online. The existing testing indicator, model, and application scenario combined with the practice in the state grid industry were illustrated, in which the process, models, and methods were shown, and also suggestions were given on promoting the blockchain-based smart contract testing evaluation in the energy industry.
Smart meters in advanced metering infrastructure (AMI) collect fine-grained, real-time power consumption data may result in user privacy leakage. Aiming at the problem that existing privacy protection schemes cannot resist quantum attacks, an antiquantum privacy protection scheme in AMI of smart grid based on consortium blockchain and ring learning with errors (RLWE) is proposed. The scheme has the following contributions. First, the RLWE-based secure communication scheme is designed in this article, which ensures the resistance of the scheme to quantum attacks. Second, all smart meters in AMI use anonymous identity to communicate with the control center, and the fine-grained power consumption data is also encrypted and digitally signed to prevent user privacy leakage and tampering of power consumption data. And the scheme does not use bilinear pairings and exponential operations and has efficient computational performance. Third, a two-tier consortium blockchain storage system consisted of regional gateways and data aggregators are designed to eliminate the single point of failure problems of the data aggregators. The practical byzantine fault tolerance consensus mechanism is improved to reduce the communication cost in the consensus process. The scheme's security is proven theoretically, and the experimental results show that the scheme has a low computational cost.
Tehseen Mazhar, Hafiz Muhammad Irfan, Sunawar Khan, Inayatul Haq · 7 authors
Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.
Smart internet of things (IoT) devices are used to manage domestic and industrial energy needs using sustainable and renewable energy sources. Due to cyber infiltration and a lack of transparency, the traditional transaction process is inefficient, unsafe and expensive. Smart grid systems are now efficient, safe and transparent owing to the development of blockchain (BC) technology and its smart contract (SC) solution. In this study, federated learning extreme gradient boosting (FL-XGB) framework has been developed along with BC to learn the intrusion inside the smart energy system. FL is best suited for a decentralized BC-enabled system to adapt learning models for trustworthy and reliable transactions. Many features and attributes of the Third International Knowledge Discovery and Data mining Tools Competition (KDD Cup 1999) dataset have been used in this study to perform experimental analysis. The likelihood of intrusions in the network is mathematically stated. The participant nodes run the BC based FL-Smart Contract (SC) algorithms to detect network intrusions. FL provided aggregated learning results from the experiment that was 99% accurate in predicting network intrusion. The experimentally determined block storage gain and retrieval gain were 97.5% and 95.4% respectively. The intrusion in the smart grid network was evaluated, and the data indicated that there was 1.2% illegal access. Moreover, the learning system’s accuracy, retrieval and storage intrusions, legal access and transaction processing times were considered for comparison. The proposed system outperformed contemporary research-developed systems targeted for the same application. Therefore, this study provides a guaranteed intrusion learning system and secure transaction system for smart grids.
With the increasing popularity of Ethereum, smart contracts have become a prime target for fraudulent activities such as Ponzi, honeypot, gambling, and phishing schemes. While some researchers have studied intelligent fraud detection, most research has focused on identifying Ponzi contracts, with little attention given to detecting and preventing gambling or phishing contracts. There are three main issues with current research. Firstly, there exists a severe data imbalance between fraudulent and non-fraudulent contracts. Secondly, the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts. Lastly, most prior studies have used contract source code as raw features, but many smart contracts only exist in bytecode. To address these issues, we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet (ECA-EfficientNet) and data enhancement. Our method begins by converting bytecode into Red Green Blue (RGB) three-channel images and then applying channel exchange data enhancement. We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images. Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi, honeypot, gambling, and phishing smart contracts. The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection. Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.
Bitcoin has a reputation of being used for unlawful activities, such as money laundering, dark web transactions, and payments for ransomware in the context of smart cities. Blockchain technology prevents illegal transactions, but cannot detect these transactions. Anomaly detection is a fundamental technique for recognizing potential fraud. The heuristic and signature-based approaches were the foundation of earlier detection techniques, but tragically, these methods were insufficient to explore the entire complexity of anomaly detection. Machine Learning (ML) is a promising approach to anomaly detection, as it can be trained on large datasets of known malware samples to identify patterns and features of the transactions. Researchers are focusing on determining an efficient fraud and security threat detection model that overcomes the drawbacks of the existing methods. Therefore, ensemble learning can be applied to anomaly detection in Bitcoin by combining multiple ML classifiers. In the proposed model, the ADASYN-TL (Adaptive Synthetic + Tomek Link) balancing technique is used for data balancing. Random search, grid search and Bayesian optimization are used for hyperparameter tuning. The hyperparameters have a great impact on the performance of the model. For classification, we used the stacking model by combining Decision Tree, Naive Bayes, K-Nearest Neighbors, and Random Forest. We used SHapley Additive exPlanation (SHAP) to interpret the predictions of the stacking model. The model also explores the performance of different classifiers using accuracy, F1-score, Area Under Curve-Receiver Operating Characteristic (AUC-ROC), precision, recall, False Positive Rate (FPR) and execution time, and ultimately selects the ideal model. The proposed model contributes to the development of effective fraud detection models that address the limitations of the existing algorithms. Our stacking model, which combines the prediction of multiple classifiers, achieved the highest F1-score of 97%, precision of 96%, recall of 98%, accuracy of 97%, AUC-ROC of 99% and FPR of 3%.
This paper reviews the methods to identify unauthorized cryptocurrency mining customers in distribution networks. Hence, various methods and models were compared. Also, the research conducted in the field of cryptocurrencies was evaluated in the four categories of energy, pollution, theft, and mining.
Ahmed Laftimi, Hind El Makhtoum, Raouya Aknin, Youssef Bentaleb
Smart grids, as all other IoT applications, are subject to numerous security challenges because of their component's constrained nature, namely smart meters, which are the weakest link in the network. Although several researchers have tried to solve the authentication issues, they did not provide a complete solution that ensures, at the same time, integrity, availability, and confidentiality of the authentication data and allows the mobility of the smart meters. The new architecture proposed in this article aims to strengthen the authentication process by adding blockchain and artificial intelligence to the current one. The artificial intelligence algorithms implemented in the MDMS server allow the detection of authentication fraud and therefore deny access. Moreover, in addition to storing the authentication data in an immutable ledger, the decentralized nature of the blockchain allows smart meters to connect to any MDMS server belonging to the cluster. The authentication policy is implemented in the smart contract and executed by the blockchain nodes. The other architecture components, namely, the smart meters and the MDMS servers, interact with this smart contract through transactions. The article also introduces the concept of the smart meter reputation, which prevents the authentication of a malicious smart meter. The smart contract used for this purpose is implemented on the remix as Ethereum integrated development environment. The simulation tests have shown the architecture's robustness and resistance against different attacks.
Athira Jayavarma S, P. K. Preetha, Manjula G. Nair
By having power consumption closer to the source of generation and preventing losses in transmission and distribution, decentralized energy production and consumption significantly helps to save the planet's ecosystem and climate. Emerging intelligent technologies that support energy transactions can expedite the growth and speed up the use of distributed eco-friendly energy sources. Distributed energy technologies are crucial since they are built specifically to generate, store, and distribute green energy. The integration of blockchain technology in the electrical power and energy domain have the capability to promote the adoption of renewable energy sources and offer a solid foundation for tracking real-time data on energy utilization. Additionally, blockchain can facilitate trade between autonomous actors, which could develop more secure energy communities. Smart contracts have become an interesting research area in various fields due to the increased popularity and expanded applications of blockchain technology. In this paper, the advantages of blockchain and smart contracts in energy trading are emphasized. This paper gives an overview of different techniques and areas to be considered for implementing smart contracts for energy transactions.
We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.
Blockchain technology has changed the global trading of assets. A blockchain can be viewed as a connected ledger managed by a distributed peer-topeer (P2P) network. Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others. These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) The increasing desire for technological advancements stimulated the development of BT.
Since the beginning of the insurance business, dishonest practices have plagued the sector. Each year, many crimes are not investigated, which costs the insurance industry billions of dollars. It is anticipated that by the year 2020, the Indian insurance business would have grown to a value of 21,715 billion dollars thanks to improvements in both knowledge and distribution methods. India comes up at number 10 for the amount of gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result, a blockchain technology-based architecture is developed to enable safe transactions and the data interchange between many agents make up the insurance network. The blockchain is a decentralized peer-to-peer technology that enables the secure, unchangeable, and open-source authentication of healthcare claims. Consider the ways in which blockchain technology and smart contracts could make corporate operations more effective. It will demonstrate how these technologies may be integrated to construct a system that makes use of machine learning to identify and prevent fraud in a variety of fields, including automobile, healthcare, and life insurance claims, among others.
Peter M. Resch, Christian Schroeder, A. Pourmovahed, Kristy Brinker Brouwer
Since the anonymous person or group known as Satoshi Nakamoto created Bitcoin in 2008, interest in the cryptocurrency has grown exponentially. In the past 14 years, thousands of additional cryptocurrencies have been created, some still active while others have faded into obscurity. Investors have flocked to cryptocurrencies for a variety of reasons from technology to decentralization to a hedge against inflation to simply chasing profits. For all the proponents that Bitcoin and other cryptocurrencies have, they also have their share of critics. Recently, many people around the world have criticized cryptocurrencies for the amount of energy that they consume for new coins to be mined. This article aims to assess current data regarding energy usage for mining cryptocurrencies as well as, understand what actions are being taken to reduce the carbon footprint of these cryptocurrency mining activities around the world.