Edwin Mahendra, Hrithik Madan, Sonal Gupta, Sajai Vir Singh
The rapid development of digital currency especially crypto currency during the last decade is the most controversial development in the modern global economy. Because of the high volatile market and fluctuations in Bitcoin price, it has led a lot of confusion among the investors. This paper proposes the novel method of the construction of prediction model using deep learning approach. The proposed approach was found to be more accurate than the Machine learning models used for prediction as the deep learning model consider the non-linear nature of price. The results verify the applicability of model and give a direction to investors on how deep learning techniques can be used in decision making.
As businesses grow, trust among participating stakeholders assumes prime importance. The transparency and efficiency in the transactions that occur in these businesses are equally valuable as the profits. Digitization of the economy helps ease-out the conduct of business; however, the increase in vulnerability to cyber-attacks is also on the rise. Blockchain technology revolutionizes the way digital transactions can occur and holds much promise in securing the flow of information that primarily drives them. Such a distributed ledger framework allows the traceability of a transaction through its immutable chain of blocks. Each block registers a time-stamped information set, verified by all the stakeholders involved in the business. These sets of features and much more make the blockchain technology an immensely powerful force to bring in transparency, efficiency, and trust in various industries. The paper's primary focus is given the features that empower a blockchain to facilitate various tasks securely, efficiently, and smoothly in a power sector. It also gives impressions of pilot projects in India with blockchain applications in different sectors. Glimpses of a prototype developed by the authors for managing the trading of rooftop solar energy among a set of consumers have been provided as one of the applications in power distribution.
There are large numbers of vehicles in the populated country like India. It's a very common scenario that traffic police came across some vehicle random vehicle and had some doubt in mind but do not have in hand information about that vehicle and end up leaving that thought. Sometimes this may result in some disaster. With the advent of technology, there are mobile applications and web based systems are available to ease up the process by which traffic police can fine the vehicle owner or people can pay the fine online. But yet there is no system is available through which traffic police can get all the details about the particular vehicle. This motivated us to design and developed an application thorough which traffic police can get all the information right from owner of the vehicle to its RC book and insurance status on just one click. Looking at the chances of data tampering, we have also played an attention to the data security and used blockchain for creating distributed, robust and tempered proof system. In this paper we have discussed traffic police assistance system, which can scan the vehicle number plate, identify the number and provide the all the information and documents stored against that vehicle number. To address the issue of data security and alteration of sensitive data blockchain is used so that any alteration can be monitored. As the complete information process is dependent on how correctly the vehicle number is identified, so the number plate recognition module is tested thoroughly under various conditions. Finally user feedback is taken and analyzed to evaluate the feasibility and usability of the proposed application.
Food traceability has gained widespread prominence in recent years due to the widely reported food fraud and safety issues and there is a need for a simple low-cost solution. Adopting the design science research methodology, we proposed a design-based set of practical criteria for food tracking system that includes the prevention of location data input fraud. The design was successfully implemented on a blockchain as food tracking system. Based on our evaluation, the design and prototype demonstrate a viable approach to food tracking and limiting food fraud.
Abdelaziz Elbaghdadi, Soufiane Mezroui, Ahmed El Oualkadi
The cryptocurrency is the first implementation of blockchain technology. This technology provides a set of tracks and innovation in scientific research, such as use of data either to detect anomalies either to predict price in the Bitcoin and the Ethereum. Furthermore, the blockchain technology provide a set of technique to automate the business process. This chapter presents a review of some research works related to cryptocurrency. A model with a KNN algorithm is proposed to detect illicit transaction. The proposed model uses both the elliptic dataset and KNN algorithm to detect illicit transaction. Furthermore, the elliptic dataset contains 203,769 nodes and 234,355 edges; it allows to classify the data into three classes: illicit, licit, or unknown. Each node has associated 166 features. The first 94 features represent local information about the transaction. The remaining 72 features are called aggregated features. The accuracy exceeded 90% with k=2 and k=4, the recall reaches 56% with k=3, and the precision reaches 78% with k=4.
In order to better realize agricultural informatization, a data management system for agricultural supply chain, which has the characteristics of safe, credible, stable, traceable, information-sharing, and large-throughput, is needed to construct. To date, the information management method for China's agricultural supply chain is usually stored in a centralized database and file system, with weak information management capabilities, leading to problems such as theft, tampering, deletion, and inconsistencies. In light of these problems, we introduce blockchain technology which is a cutting-edge technology in digital finance and has developed rapidly. This paper proposes a data management system based on blockchain technology and affords efficient data extraction, management and access control for heterogeneous forms of data across the agricultural supply chain. The data management system includes four credible data management platforms: agricultural production information, recording transportation information, farmer-consumer transaction information, and consumer credit information. It also can add more platforms according to later needs and combine an interstellar distributed file system and smart contract technology, making it possible to conduct information security research throughout the agricultural supply chain. The proposed system effectively protects information about supply chain activities including agricultural product production, warehousing, transportation, distribution, and sales. It implements seamless connection between agricultural product production and marketing, enabling the transforming and upgrading of agriculture, thereby helping farmers increase their income and eliminate poverty.
In recent years, bitcoin has been at the center of a debate: is it a financial asset or not? If it is an asset, what kind of asset is it? Is it a tradable, speculative asset, or is it a long-term investment? The debate has largely unfolded around the real nature of Bitcoin and its correlation with other asset classes. Some scholars have discussed the correlation coefficient of bitcoin using the S& P 500 Index, the Volatility Index, and gold. However, the debate is now sufficiently mature to discuss the reasons determining its correlation with other assets. We study the correlations of Bitcoin and one benchmark, a stock market index-specifically, the Nasdaq 100 Index. Our focus is on the correlation between Bitcoin and the Nasdaq 100 as a proxy for the most representative technology stocks. We show that the correlation between the two has grown substantially since 2018, and has increased more since the beginning of the COVID-19 pandemic.
As one of the leading blockchain systems in operation, Ethereum has numerous smart contracts deployed to implement a variety of functions. Unfortunately, speculators introduce scams such as Ponzi scheme in the traditional financial sector into some of these smart contracts, causing millions of dollars of losses to investors. At present, there are a few of quantitative identification methods for new fraud modes under the background of Internet finance, and detection methods for the Ponzi scheme contracts on Ethereum are even less. In this paper, we propose an improved convolutional neural network as a detection model for Ponzi schemes in smart contracts. We use real smart contracts to evaluate the feasibility and usefulness of our mode. Results show that our improved convolutional neural network can overcome difficulties in training caused by different length of smart contracts' bytecodes. Compared with the state-of-the-art methods, the precision and recall rate of our model for Ponzi scheme detection are improved by 3.2% and 24.8% respectively.
This paper is discusses the problems of the short-term forecasting of financial time series using supervised machine learning (ML) approach. For this goal, we applied several the most powerful methods including Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forests (RF) and Stochastic Gradient Boosting Machine (SGBM). As dataset were selected the daily close prices of two stock index: SP 500 and NASDAQ, two the most capitalized cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and exchange rate of EUR-USD. As features we used only the past price information. To check the efficiency of these models we made out-of-sample forecast for selected time series by using one step ahead technique. The accuracy rates of the forecasted prices by using ML models were calculated. The results verify the applicability of the ML approach for the forecasting of financial time series. The best out of sample accuracy of short-term prediction daily close prices for selected time series obtained by SGBM and MLP in terms of Mean Absolute Percentage Error (MAPE) was within 0.46-3.71 %. Our results are comparable with accuracy obtained by Deep learning approaches.
At present, the traceability of food information in China is the traceability of a specific product, which lacks universality. Therefore, in order to trace the food information used in major activities, this paper proposes a one-code-one-item food information traceability system. Call Alibaba Cloud's commodity bar code query API to obtain the original information of the product; use the food information traceability software designed in this article to generate an exclusive code for each product to achieve precise identification of one-code-one-item; collect information on subsequent "transaction" links of product warehousing, transportation, testing and processing to ensure the integrity of food traceability information; use AES encryption method to encrypt food traceability information; utilize the distributed fault tolerance, immutability and privacy protection features of blockchain technology to store the encryption key on the chain to avoid illegal tampering and forgery of information and ensure data security.
Various ways have been done by researchers from all over the world to predict the price of Bitcoin which is the first digital commodity based on blockchain. Various forecasting and computational techniques have been widely applied and continue to be refined to find a prediction package that has the closest accuracy. The purpose of this research is to test hundreds of API data shared by hundreds of Cryptocurrency Markets and as a result we have succeeded in extracting the latest bitcoin price data from 20 Crypto Markets via shared APIs. This data will then be stored in a MySQL database on hosting automatically when a condition is reached. This data can then be mined continuously using Cronjob, and to facilitate the reading of data that has been collected, we use a telegram bot as well as using a web-based application. This step is expected to help in efforts to monitor bitcoin price movements and predict future price movements.
Blockchain is a digital ledger in which each record known as blocks and that are combined in a single list known as a chain. It is regarded as Bitcoin's backbone technology. It is also regarded as cohesive collections of digital wallets. Blockchains are primarily used by cryptocurrencies such as Bitcoin and other applications to record these transactions. A blockchain is commonly referred to as a collection of distributed databases that consists of all public transactions, records and digital events then that information is shared among the participants. Every transaction is verified and it cannot be removed. The main features of this technology are reliable, efficient operation, fault tolerance and scalability. Some of the applications are manufacturing, government and finance when the three properties met together (i.e., Efficiency, Scalability and Security). By using several computers, each transaction that is applied to a blockchain is validated. A peer-to-peer network is developed by these systems that are used to validate these forms of blockchain transactions. They work together to ensure that any transaction is legitimate until it is added to the blockchain, and invalid blocks cannot be added to the chain by these systems. When a new block is added, it can be connected to a previous block using a cryptographic hash and the chain cannot be broken and each block is recorded permanently. Blockchain can be used for an exchanging the transaction securely without an intermediate. It enables customer relationship and agile chain values and thereby integrating with IoT and Cloud technology. The functionality of distributed ledger is combined with blockchain security to solve the financial and non-financial industry problems. This paper proposes the blockchain technology with devices and creates a common platform and secure data communication.
Ricardo Carreño Aguilera, Miguel Patiño-Ortiz, Julián Patiño-Ortiz, ADAN ACOSTA BANDA
Blockchain technology apparently is a trivial innovation, but this technology has attracted huge investors in a very short period compared to other technologies, and it is still having a lot of potential applications. Smart contracts are making possible execution in an automated and safe way by using blockchain technology. Therefore, smart contracts are applied in this research for the expert system. This paper is about an expert system working with smart contracts and neural networks as the inference machine to decide on the sensors optimal distribution and taking actions when sensor readings are out of range: control lights, activating fire alarms, temperature alarms, etc. for all spaces (parks, schools, hospitals, etc.) in a smart city based on the needs, and likes of the expert system user. This expert system works using a blockchain structure on the EOSIO ecosystem with all data gathered by the sensors being saved in cloud online making internet of things environment and essential data saved in a blockchain node.
Subhi Alrubei, Edward A. Ball, Jonathan Rigelsford
The increased implementation of Edge Computing technology has provided The Internet of Things (IoT) with the ability of real-time data processing and tasks execution requested by smart devices. To support this processing the integration of Artificial Intelligence (AI) into IoT is considered one of the most promising approach. While AI helps in the analyses of the data, blockchain technology provides a robust environment within which to create a secure, distributed way to share and store data. This paper proposes an architecture that combines the strengths provided by edge computing, AI, and blockchain technologies to provide robust, secure, and intelligent solutions for secure and faster data processing and sharing. The pandemic created by the rapid spread of the novel Coronavirus COVID-19, as well as the tracking of viruses in water sewage to help control the spread of such viruses, were used as our case study for exploring this architecture. To secure the proposed architecture a new concept for consensus mechanism based on Honesty-Based Distributed Proof of Work (DPOW) were devised and tested.
Blockchain is a distributed shared ledger and database. Because of its unforgeable, traceable, open, and transparent, and collective maintenance features, it has laid a solid foundation of “trust”. At the same time, the blockchain also brings hidden dangers of data security. How to protect the data security of digital currency in blockchain transactions is the direction of this article. This article first analyzes the basic structure of the blockchain, and then takes Bitcoin in the digital currency as an example to introduce its technical principles and implementation process. Finally, the PBFT algorithm is proposed, which can search for the application of encryption in the blockchain consensus mechanism. The experimental data verification proves the future application prospects of blockchain-based digital currency using PBFT algorithm to protect data security.
A blockchain is a growing list of records, called blocks, that are linked using cryptography. Blockchain private and public keys are stored in a cryptocurrency wallet, but not the actual currency values. Wallets provide customers with the ability to send and receive virtual currency / tokens and tune their balance through interaction with blockchains. Multi-currency wallets may be broken down into 3 categories: software, hardware, and paper. Software wallets are web, mobile and desktop. Growing penetration of blockchain in many industries makes one to understand wallets in detail. There are a variety of wallet kinds to pick out from. This paper focuses on multi-currency wallets review exploring on features like supported currencies, anonymity, cost, platform support, key management, wallet recovery methods and fiat currencies supported.
Sazeen Taha Abdulrazzaq, Farooq Safauldeen Omar, Maral A. Mustafa
Since the introduction of blockchain, cryptocurrencies have become very attractive as an alternative digital payment method and a highly speculative investment.With the rise in computational power and the growth of available data, the artificial intelligence concept of deep neural networks had a surge of popularity over the last years as well.With the introduction of the long short-term memory (LSTM) architecture, neural networks became more efficient in understanding long-term dependencies in data such as time series.In this research paper, we combine these two topics, by using LSTM networks to make a prognosis of decentralized blockchain security.In particular, we test if LSTM based neural networks can produce profitable trading signals for different blockchains.We experiment with different preprocessing techniques and different targets, both for security regression and trading signal classification.We evaluate LSTM based networks.As data for training we use historical security data in one-minute intervals from August 2019 to August 2020.We measure the performance of the models via back testing, where we simulate trading on historic data not used for training based on the model's predictions.We analyze that performance and compare it with the buy and hold strategy.The simulation is carried out on bullish, bearish and stagnating time periods.In the evaluation, we find the best performing target and pinpoint two preprocessing combinations that are most suitable for this task.We conclude that the CNN LSTM hybrid is capable of profitably forecasting trading signals for securing blockchain, outperforming the buy and hold strategy by roughly 30%, while the performance was better.The LTSM method used by current system for encrypting passwords is efficient enough to mitigate modern attacks like man in the middle attack (MITM) and DDOS attack with 95.85% accuracy
Mohd Azeem Faizi Noor, Saba Khanum, Taushif Anwar, Manzoor Ansari
Blockchain, the technology behind most popular cryptocurrency Bitcoin and Ethereum, has attracted wide attention recently. It is the most emerging technology that has changed the financial and non-financial transaction system. It is omnipresent. Currently, this technology is enforcing banks, industries, and countries to adopt it in their financial, industrial, and government section. Earlier, it solved the centralize and double-spending problems successfully. In this chapter, the authors present a study of blockchain security issues and its challenges as well. They divided the whole chapter into two parts. The primer part covers a holistic overview of blockchain followed by the later section that argues about basic operations, 51% attack, scalability issue, Fork, Sharding, Lightening, etc. Finally, they mention an intro about its adaptation (financial or non-financial) in our 24/7 life and collaboration with fields like IoT.
In today's world, security has become a major issue in our lives, and in this era, one cannot trust the government for handling their lifetime savings. That's where the bitcoin comes to our lives. In this chapter, the authors try to understand one of the famous innovative payment methods (Bitcoin), how it is used and the data structure (Merkle tree) that is used in it. Also, they discuss one of the most recent attacks that involved the use of bitcoin (Wanacry). Further, they try to understand how this hack succeeded in stealing 10,800 euros that is 8,74,290 rs from the hospital with the help of bitcoin. They also discuss the various bitcoin companies now emerging with their own security measures against such hacks.