Bitcoin is a digital currency that has enticed considerable recognition as an alternative solution to the traditional payment system. Its success is mostly due to the use of Blockchain over a decentralised peer-to-peer network. Today's Bitcoin network has more than 10,000 online peers geographically scattered around the world. However, its topology is still unknown due to security concerns. In this paper, we design and implement a fast and efficient framework named BTCmap to discover and map the Bitcoin network topology. Our BTCmap framework includes two modules viz., a sniffer to collect the local addresses database of each peer, and a Bitcoin peer emulator to select neighbours and generate the topology. Our analyses show that to remain within 1% confidence interval, we have a window of 56 minutes 40 seconds for collecting the local address databases from all peers. Within this time duration, BTCmap requests more than 8200 reachable peers to map the real Bitcoin network topology. We present a Bitcoin topology snapshot captured on June 14, 2018, 15:53 CEST and show that this snapshot is connected.
Since Bitcoin’s launch in early 2009, the industrial and academic interest in Blockchain and other cryptocurrencies have grown rapidly. Blockchains have been applied in many areas outside of finance such as healthcare, commerce and judiciary already. This technology promotes the creation of a decentralized environment where transactions and data are not under the control of any third-party organization. Blockchain is a fundamentally new technology that could revolutionize the future of transaction-based exchanges. Extensive research is being done in order to implement this technology various sectors. Blockchain technology comes with an edge of inbuilt auditability, trust and transfer of value which also makes it irresistible. \nThis work explores an agent based Blockchain-based Education System through mathematical modeling and simulation tools. The model is constructed to explore how Blockchain technology can be used to verify credit score of students, identify the occurrence and prevention of potential attacks. Along with technical characteristics of Bitcoin and Blockchain; cost, time and behavioural considerations of the system are also made. This is followed by analysing of the number of transactions, size of blockchain, network efficiency, cost analysis of the system along with the network efficiency. The proposed model, based on the blockchain technology shifts the education grading and credit rewarding system from the analog and physical world into a globally efficient, transparent and universal version. The work contributes a foundation for advancing current understanding of blockchain systems, and to further the development of simulation models of blockchains.
Aug 1, 2018·2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Blockchains such as those used by the Bitcoin and Ethereum cryptocurrencies provide a global, observable record of all transactions and associated data. Analyzing blockchain data is useful for tasks such as detecting fraudulent activities, studying the use and growth of the system, and understanding its levels of anonymity and traceability. Such analysis is challenging due to the high volume and rapidly changing characteristics of popular blockchains. In particular, online (soft real-time) analysis of blockchains requires methods that adapt organically to changes in the data. This paper describes such a method based on self-organizing maps and reports on experiments using the Bitcoin blockchain data.
Blockchain Technology Applications and Security
Data Stream Mining Techniques
Advanced Steganography and Watermarking Techniques
Aug 1, 2018·2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on
When Bitcoin's price skyrocketed then cryptocurrencies dragged the world's attention. Since then "Cryptocurrency Mining" has became a buzz word for computer geeks as well as for those who wanted to add a new source of income. "Mining" is a process of solving computational problems using hardware which results in obtaining rewards in the form digital currency. Large number of people who could afford a personal computer, started getting attracted towards mining, which resulted in formation of "Mining Pools". Mining Pools are an explicit example of teamwork where miners share their efforts to mine cryptocurrencies. This paper throws light on some of the key concepts of mining such as hash rates, pool analytics and various mining software. A complete financial report is enclosed in this paper which discusses the profitability or non-profitability of various cryptocurrencies and hardware by considering various attributes like pool fee, electricity costs, and revenues excluding the cost of hardware. This paper is an outcome of practical mining of several cryptocurrencies on GPUs and CPUs (non-custom hardware) which results in contextually slower mining as compared to high-end miners. Proposed data will guide novice miners to start mining.
With the increasing of using workflow management systems workflow improvement becomes a new emerging problem. Many issues must be considered to handle all aspects of the workflow improvement. Workflows might become quite complex, especially when we move to Web3 (ubiquitous computing web). Workflows from different domains (e.g., scientific or business) have similarities and, more important, differences between themselves. Some concepts and solutions developed in one domain may be readily applicable to the other. In ubiquitous computing, multi-domain workflow data analysis might cause Big Data challenge. This paper investigates the problem of workflow improvement having an observed behavior (i.e., event logs). It proposes a cross-domain concept extraction by similarity assessment to solve some aspects of workflow improvement problem, and it has a new research effort at the intersection of workflow domains. Besides, the proposed technique is evaluated with the benefit of using Deep learning and Transfer learning. One of the greatest assets to use these both learning methods is analyzing a massive amount of data. Our results show that our proposed technique is effectively applicable for analyzing real-life huge data in workflow improvement.
In recent years, Bitcoin is the most valuable in the cryptocurrency market. However, prices of Bitcoin have highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute interval trading data on the Bitcoin exchange website named bitstamp from January 1, 2012 to January 8, 2018, some different regression models with scikit-Iearn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2%.
Jianli Pan, Jianyu Wang, A. MARIA HESTER, Ismail AlQerm · 6 authors
The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are "weak" and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively "vulnerable" facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named "EdgeChain" based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or "coin" system to link the edge cloud resource pool with each IoT device' account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.
Jun 1, 2018·2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Energy conservation measurements in buildings are more and more popular as they benefit from an intelligent contractual framework called Energy Performance Contracts (EPC), where energy savings are measured as the difference between a predictive baseline model and the actual consumption. While modern predictive models make use of large amounts of data from external sources and increasingly complex algorithms, these two aspects make their use difficult in practice because they need mutual understanding and transparency, requiring the involvement of a third-party for auditing. In this sense, we designed and developed a prototype that overcomes these issues by storing the predictive models and the data in an immutable blockchained data structure using the Ethereum framework. To the best of our knowledge, this is the first working prototype using the blockchain technology applied to EPCs. This paper presents and discusses the technical solutions and best-practice guidelines adopted in this prototype.
Taking Ei Compendex (EI) and China National Knowledge Infrastructure (CNKI) databases as the literature sources, this paper presented a bibliographic analysis of the blockchain-related literature between January 2011 and September 2017. For each literature source, we built a separate dataset. Authors' productivity and collaboration, affiliation of authors and collaboration amongst institutions were analyzed using techniques of social networks analysis on both datasets. According to the results, the CNKI authors/institutes raised their productivity and outperformed the EI authors/institutes since 2016. However, the EI authors/institutes show a higher level than the CNKI authors/institutes in collaboration. We also summarized the hot topics on the EI dataset using textual analysis and discovered researchers have shifted their attention from Bitcoin itself to the blockchain technology underlying it.
This paper provides the first comprehensive survey of methods for inserting arbitrary data into Bitcoin’s blockchain. Historical methods of data insertion are described, along with lesser-known techniques that are optimized for efficiency. Insertion methods are compared on the basis of efficiency, cost, convenience of data reconstruction, permanence, and potentially negative impact on the Bitcoin ecosystem.
Beltrán Borja Fiz Pontiveros, Robert Norvill, Radu State
Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining pools distribute amongst the workers the task of finding a block so that each worker works on a different subset of the candidate solutions. In most mining pools the selection of transactions to be part of the next block is performed by the pool manager and thus becomes more centralized. A mining Pool is expected to give priority to the most lucrative transactions in order to increase the block reward however changes to the transaction policy done without notification of workers would be difficult to detect. In this paper we treat the transaction selection policy performed by miners as a classification problem; for each block we create a dataset, separate them by mining pool and apply feature selection techniques to extract a vector of importance for each feature. We then track variations in feature importance as new blocks arrive and show using a generated scenario how a change in policy by a mining pool could be detected.
M Vaidehi, Alivia Pandit, Bhaskar Jindal, Minu Kumari · 5 authors
After the boom and bust in cryptocurrencies’ prices in recent years, Bitcoin has been totally regarded as an investment asset. As it is highly volatile in nature, there has been a need for good predictions for carrying base investment decisions. Although current study has used machine learning for more accurate Bitcoin price prediction, some of them did focused on the feasibility of applying different modeling techniques to the samples that has different data structures and dimension features. To predict Bitcoin price on different frequencies after using machine learning techniques, firstly we have to classify the Bitcoin price with daily price and high-frequency price. Here, we attempt to predict Bitcoin price as accurately as possible by taking into consideration various protocols that affect the Bitcoin value. Using the provided data we would predict the sign of daily price change with highest possible accuracy. We have used Random Forest Classifier and compared with benchmark results as daily price prediction, we achieve a better performance, with the highest accuracies of the statistical methods and machine learning algorithms of 99%. my investigation in Bitcoin price prediction can be considered as a pilot study for the importance of the sample dimension in the machine learning techniques. Keywords Bitcoin, Crypto Currency, Machine Learning, Blockchain, Long Short Term Memory(LSTM), Recurrent Neural Network(RNN), Prediction
Giovanni Ciatto, Roberta Calegari, Stefano Mariani, Enrico Denti · 5 authors
The blockchain is a novel approach to support distributed systems enabling a common, consistent view of a shared state among distributed nodes. There, smart contracts are computer programs that allow users to deploy arbitrary computations, in charge of automatically regulate state transitions and enforce properties. In this paper we speculate on how the blockchain and smart contracts could take advantage of a logic programming approach, and, complementarily, on how logic programming can benefit from the blockchain infrastructure. Accordingly, we discuss some possible research directions and open questions for future research.
Blockchain technologies have the potential to establish novel financial service infrastructures and reshape numerous fields. A blockchain is essentially a distributed ledger maintained by a set of peers (i.e., trading nodes) that do not fully trust each other. A key challenge that blockchain faces is to precisely classify the blockchain peers into categories with respect to their behavior patterns, which will not only enable deeper insights into the blockchain network but also facilitate more effective maintenance of the various peers (in private chains). In this paper, we introduce and formulate the problem of behavior pattern classification in blockchain networks and propose a novel deep-learning-based method, termedPeerClassifier, to address the problem. To the best of our knowledge, we are the first to formally define the problem of peer behavior classification in blockchain networks. Moreover, we conduct extensive experiments to evaluate our proposed approach. Experimental results demonstrate thatPeerClassifieris significantly more effective than the existing conventional methods.
Francisco De La Vega, Javier Soriano, Miguel Jiménez, David Lizcano
Modern IoT deployments do require considerable investments that might only be justified if the data being gathered could be monetized, which leads to the need for a digital data marketplace. In many cases, the provider of the IoT data needs to process it locally for data curation, aggregation, stream processing, etc. At the same time, the consumer could be interested in nearby data. This scenario resembles a fog computing architecture where companies require being able, keeping data under their control, to securely make it available to other companies in a peer‐to‐peer fashion, without needing a cloud intermediary (like traditional marketplaces do), thus maximizing the locality of the processing and avoiding the existence of a bottleneck when the intermediary makes the data delivery for accounting purposes. Nevertheless, this imposes a hard requirement: by not having a central marketplace, the peers (seller and customer) need to trust each other, which, in turn, requires enforcing a nonrepudiation schema. In this paper, the authors propose a distributed peer‐to‐peer architecture for such a data marketplace that takes advantage of the architectural fundamentals of fog computing, in which data processing, filtering, and stream based event generation is done in a fog node along with the data, and where relationships, both commercial agreements and data delivery, are performed directly between producers and consumers without the need of mutual trust thanks to the usage of blockchain principles (e.g., distributed ledger, consensus mechanism). The proposed architecture is validated through a case study involving a set of key issues regarding nonrepudiation commonly identified when moving from a centralized marketplace to a distributed one. Moreover, it is shown that the proposed solution does not bring in any limitation with regard to a centralized marketplace solution, in terms of pricing models (subscriptions, pay‐per‐use, etc.) or usage conditions (contract duration, updates rate, etc.).
Cüneyt Gürcan Akçora, Matthew Dixon, Yulia R. Gel, Murat Kantarcıoğlu
A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.
The concept of Bitcoin was first introduced by an unknown individual (or a group of people) named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is a peer-to-peer cryptocurrency and a decentralized worldwide payment system for digital currency where transactions take place among users without any intermediary. Bitcoin transactions are performed and verified by network nodes and then registered in a public ledger called blockchain, which is maintained by network entities running Bitcoin software. To date, this cryptocurrency is worth close to U.S. $150 billion and widely traded across the world. However, as Bitcoin's popularity grows, many security concerns are coming to the forefront. Overall, Bitcoin security inevitably depends upon the distributed protocols-based stimulant-compatible proof-of-work that is being run by network entities called miners, who are anticipated to primarily maintain the blockchain (ledger). As a result, many researchers are exploring new threats to the entire system, introducing new countermeasures, and therefore anticipating new security trends. In this survey paper, we conduct an intensive study that explores key security concerns. We first start by presenting a global overview of the Bitcoin protocol as well as its major components. Next, we detail the existing threats and weaknesses of the Bitcoin system and its main technologies including the blockchain protocol. Last, we discuss current existing security studies and solutions and summarize open research challenges and trends for future research in Bitcoin security.