The cryptocurrency market is a very huge market without effective supervision. It is of great importance for investors and regulators to recognize whether there are market manipulation and its manipulation patterns. This paper proposes an approach to mine the transaction networks of exchanges for answering this question. By taking the leaked transaction history of Mt. Gox Bitcoin exchange as a sample, we first divide the accounts into three categories according to its characteristic and then construct the transaction history into three graphs. Many observations and findings are obtained via analyzing the constructed graphs. To evaluate the influence of the accounts' transaction behavior on the Bitcoin exchange price, the graphs are reconstructed into series and reshaped as matrices. By using singular value decomposition (SVD) on the matrices, we identify many base networks which have a great correlation with the price fluctuation. When further analyzing the most important accounts in the base networks, plenty of market manipulation patterns are found. According to these findings, we conclude that there was serious market manipulation in Mt. Gox exchange and the cryptocurrency market must strengthen the supervision.
Bruno Tavares, Filipe Figueiredo Correia, André Restivo
The explosion of blockchain projects in last couple of years shows the general interest in the blockchain technology. Looking towards the current state of the art regarding this technology it becomes clear that the main driver of innovation is the private sector. We believe that understanding main applications of the technology, academic contributions, and private solutions can reveal where the interest in the technology exists and where it can be missing. In particular, this work can help identify open source projects that can provide a framework with next-generation features as; lightning network, directed acyclic graph, mobile compatibility, or compute protocols. New applications of the blockchain technology are still being discovered regularly and in this study several blockchain development frameworks were found. However, in the academic world there are few references to operational, testing, and deployment framework related with the technology. With the expected growth of the technology, integration with preexisting solutions, legacy systems replacement, or new implementations, the need for testing, deploying, exploration, and maintenance is expected to intensify for the technology.
Jan 1, 2019·2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON)
This research represents a model of buying and selling lotteries system on Blockchain by using Ethereum network to determines the conditions for the purchasing of the lottery and reward the prize winner. Our system uses Ethereum coins to buy and reward the prize for convenience in term of speed and also reduce the problems which is unable to be controlled by the government. For example, lottery agents (intermediaries) may sell the lottery at overprice, and reward the winners at a lower than appropriate rate. Additionally, the most important problem is the verification of the real owner of the lottery.
With the rapid development of the Internet and the Internet of Things, product recall, especially pharmaceutical recall, have attracted various stakeholders as a phase of product service. However, there are some the long-term problems on pharmaceutical recall, such as low effectiveness, seldom transparent, data can be tampered easily and so on. We have proposed a service system of pharmaceutical recall based on blockchain to solve these problems. The solution runs through the entire recall process that includes decision making, the OOS/OOT investigation, the action of working group and the Corrective and Preventative Action (CAPA). Through the management of recall, we are capable of shortening time, enhancing transparency and protecting the integrity of data in pharmaceutical recall.
Applications of blockchain technologies got a lot of attention in recent years. They exceed beyond exchanging value and being a substitute for fiat money and traditional banking system. Nevertheless, being able to exchange value on a blockchain is at the core of the entire system and has to be reliable. Blockchains have built-in mechanisms that guarantee whole system's consistency and reliability. However, malicious actors can still try to steal money by applying well known techniques like malware software or fake emails. In this paper we apply supervised learning techniques to detect fraudulent accounts on Ethereum blockchain. We compare capabilities of Random Forests, Support Vector Machines and XGBoost classifiers to identify such accounts basing on a dataset of more than 300 thousands accounts. Results show that we are able to achieve recall and precision values allowing for the designed system to be applicable as an anti-fraud rule for digital wallets or currency exchanges. We also present sensitivity analysis to show how presented models depend on particular feature and how lack of some of them will affect the overall system performance.
N. Sai Sirisha, Tarasha Agarwal, Ranjeet Monde, Richa Yadav · 5 authors
The lack of transparency has made people lose trust in charities, making social funding stagnant. The donor is unaware of the legitimate utilization of his funds. Corruption adds to the distrust of the donor. This paper proposes a system called Charity-Chain that is a decentralised network built on the Ethereum blockchain. It helps social organisations to run projects transparently, using smart contract-based incentives to ensure their impact is independently verified and accessible to everyone. This makes it much easier for funders (philanthropic organisations, impact investors, small donors) to monitor their transactions and hence restore their trust in giving to such social organizations.
Santiago Bragagnolo, Matteo Marra, Guillermo Polito, Elisa Gonzalez Boix
Analysing the blockchain is becoming more and more relevant for detecting attacks and frauds on cryptocurrency exchanges and smart contract activations. However, this is a challenging task due to the continuous growth of the blockchain. For example, in early 2017 Ethereum was estimated to contain approximately 300GB of data [1], a number that keeps growing day after day. In order to analyse such ever-growing amount of data, this paper argues that blockchain analysis should be treated as a novel type of application for Big Data platforms. In this paper we explore the application of parallelization techniques from the Big Data domain, in particular Map/Reduce, to extract and analyse information from the blockchain. We show that our approach significantly improves the index generation by 7.77 times, with a setup of 20 worker nodes, 1 Ethereum node and 1 Database node. We also share our findings of our massively parallel setup for querying Ethereum in terms of architecture and the bottlenecks. This should help researchers setup similar infrastructures for analysing the blockchain in the future.
In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one model to another model that use LSTM will take a very long time, thus a method is needed for optimizing hyperparameter to get a model with a small RMSE. This research proposed Artificial Bee Colony (ABC) as a method in optimizing hyperparameter for models that use LSTM. ABC is a metaheuristic method that mimics the behavior of bee colonies in foraging. Optimized hyperparameter in this research consisted of sliding window size, number of LSTM units, dropout rate, regularizer, regularizer rate, optimizer and learning rate. In this research the proposed method called as ABC-LSTM. Bitcoin prices historical data was used as the dataset for evaluating the prediction of the models. The best ABC-LSTM model resulted best RMSE of 189.61 compared to model that use LSTM without optimization resulted best RMSE of 236.17. This result showed that ABC-LSTM model outperformed models that use LSTM without optimization.
In the wake of the financial crisis of 2008, Bitcoin emerged as a radical new alternative to the fiat currencies of the traditional banking sector. Through the use of a novel kind of probabilistic consensus algorithm, Bitcoin proved it possible to guarantee the integrity of a digital currency by relying on network majority votes instead of trusted institutions. By showing that it was technically feasible to, at least to some extent, replace the entire banking sector with computers, many significant actors started asking what else this new technology could help automate. A subsequent, seemingly inevitable, wave of efforts produced a multitude of new distributed ledger systems, architectures and applications, all somehow attempting to leverage distributed consensus algorithms to replace trusted intermediaries, facilitating value ownership, transfer and regulation. In this thesis, we scrutinize distributed ledger technologies in terms of how they could help facilitate the digitization of contractual cooperation, especially in the context of the supply chain and manufacturing industries. Concretely, we consider them from three distinct technical perspectives, (1) performance, (2) interoperability and (3) integration. Voting systems, with or without probabilistic mechanisms, require significant time and resources to operate, for which reason it becomes relevant to investigate how the costs of running those systems can be mitigated. In particular, we consider how a blockchain, a form of distributed ledger, can be pruned to in order to reduce disk space requirements. Furthermore, no technical system part of a larger business is an island, but will have to be able to interoperate with other systems to maximize the opportunity for automation. For this reason, we also consider how transparent message translation between systems could be facilitated, as well as presenting a formalism for expressing the syntactic structure of message payloads. Finally, we propose a concrete architecture, the Exchange Network, that models contractual interactions as negotiations about token exchanges rather than as function invocations and state machine transitions, which we argue lowers the barrier to compatibility with conventional legal and business practices. Even if no more trusted institutions could be replaced by any forthcoming distributed ledger technologies, we believe contractual interactions becoming more digital would lead to an increased opportunity for using computers to monitor, assist or even directly participate in the negotiation, management and tracking of business agreements, which we see as more than enough to warrant the cost of further developing of the technology. Such computer involvement may not just save time and reduce costs, but could also enable new kinds of computer-driven economies. In the long run, this may enable new levels of resource optimization, and not just within large organizations, but also smaller companies, or even the homes of families and individuals.
In today’s era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.
Learning analytics and data mining require gathering and exchanging learner data for further processing and designing of activities tailored to learner’s characteristics, context, and needs. Currently, systems that store learners’ attributes should, ideally, be operated and controlled by responsible and trustworthy authorities that guarantee the protection and sovereignty of data and use objective criteria to protect and represent all parties’ interests. This chapter introduces a peer-to-peer method for storing and exchanging learner data with minimal trust. The proposed approach, underpinned by the Experience API standard, eliminates the need of a mediator authority by using distributed ledger technology.
Blockchain is a distributed ledger system composed of a P2P network and is used for a wide range of applications, such as international remittance, inter-individual transactions, and asset conservation. In Blockchain systems, tamper resistance is enhanced by the property of transaction that cannot be changed or deleted by everyone including the creator of the transaction. However, this property also becomes a problem that unintended transaction created by miss operation or secret key theft cannot be corrected later. Due to this problem, once an illegal transaction such as theft occurs, the damage will expand. To suppress the damage, we need countermeasures, such as detecting illegal transaction at high speed and correcting the transaction before approval. However, abnormality detection in the Blockchain at high speed is computationally heavy, because we need to repeat the detection process using various feature quantities and the feature extractions become overhead. In this paper, to accelerate abnormality detection, we propose to cache transaction information necessary for extracting feature in GPU device memory and perform both feature extraction and abnormality detection in the GPU. We employ abnormality detection using K-means algorithm based on the conditional features. When the number of users is one million and the number of transactions is 100 millions, our proposed method achieves 37.1 times faster than CPU processing method and 16.1 times faster than GPU processing method that does not perform feature extraction on the GPU.
This paper proposes a framework for collaborative anomaly detection on Blockchain. Taking condition-based management of industrial asset as a practical example, we extend the notion of Smart Contract, which has been implicitly assumed to be deterministic, to be able to handle noisy sensor data. By formalizing the task of collaborative anomaly detection as that of multi-task probabilistic dictionary learning, we show that major technical issues of validation, consensus building, and data privacy are naturally addressed within a statistical machine learning algorithm. We envision Blockchain as a platform for collaborative learning rather than just a traceable, immutable, and decentralized data management system, suggesting the direction towards "Blockchain 3.0".
The aim of this paper is to propose an Internet of Things system that collects, sends, stores, and publishes relevant data using a Raspberry Pi as the smart sensor. Distributed Ledger Technologies (DLT) from BigchainDB and IOTA are used to store data in a blockchain-like database, and to publish a temporal statistic data summary respectively, in addition to store the data streams in a SQL database. The collection, storage, and publication of the data is free and almost instantaneous. This system can become a source of evidences to different types of stakeholders all around specific businesses getting benefits from keeping the level of trust along its value chain.
Global payment systems powered by blockchain technology through various cryptocurrencies like Bitcoin, Litecoin, Ethreum are growing rapidly providing public access to its database. The technology eliminates the need for a central authority like bank to control the data. Instead, verification and integrity of data are provided through cryptographic techniques. Bitcoin is not a monetary generated by a government. Analyzing the public dataset and exploring its statistical property can uncover some hidden facts in cryptocurrency and the blockchain protocol. There are two underlying motivations for us to write this survey. First is to provide an up-to-date literature survey on Bitcoin transaction analysis, and second is to understand the hidden facts that could be revealed by analyzing Bitcoin transactions. To provide a comprehensive survey, we categorize existing analysis based on anonymity, user behavior, wealth accumulation and currency rate prediction. Analyzing the Bitcoin transactions can help to understand it from a broader perspective that can help investors in decision making.
2 source records
Blockchain Technology Applications and Security
Data Stream Mining Techniques
Advanced Steganography and Watermarking Techniques
Roberto Casado‐Vara, Fernando De la Prieta, Javier Prieto, Juan M. Corchado
Smart home presents a challenge in control and monitoring of its wireless sensors networks (WSN) and the internet of things (IoT) devices which form it. The current IoT architectures are centralized, complex, with poor security in its communications and with upstream communication channels mainly. As a result, there are problems with data reliability. These problems include data missing, malicious data inserted, communications network overload, and overload of computing power at the central node. In this paper a new architecture is presented. This architecture based in blockchain introduce the edge computing layer and a new algorithm to improve data quality and false data detection.
We study the probabilistic distribution of the confirmation time of Bitcoin transactions, conditional on the current memory pool (i.e., the queue of transactions awaiting confirmation). The results of this paper are particularly interesting for users that want to make a Bitcoin transaction during `heavy-traffic situations', when the transaction demand exceeds the block capacity. In such situations, Bitcoin users tend to bid up the transaction fees, in order to gain priority over other users that pay a lower fee. We argue that the time until a Bitcoin transaction is confirmed can be modelled as a particular stochastic fluid queueing process (to be precise: a Cramér-Lundberg process). We approximate the queueing process in two different ways. The first approach leads to a lower bound on the confirmation probability, which becomes increasingly tight as traffic decreases. The second approach relies on a diffusion approximation with a continuity correction, which becomes increasingly accurate as traffic intensifies. The accuracy of the approximations under different traffic loads are evaluated in a simulation study.