Manasa Sastry J. K, Astha Pandey, M. S. Dahiya, L Magwa M
Background: From the time immemorial there have been several types of crimes. With the advancement in science and technology, digital crimes have become very prominent. One among which is Bit-Coin crypto-currency frauds which are gaining momentum in the types of frauds encountered by law enforcement agencies. Bit-Coin is a growing form of digital crypto-currency that is created and held electronically that has no centralized control systems, that governs the transactions. It is the most secretive form of money transfer between two anonymous people all over the world. It is on a superficial layer used to purchase or sell goods electronically, similar to the conventional dollars that are traded digitally where individual ledgers are maintained by all the bit coin users to have access to the building block-chain. However, a masked layer consists of a dark-net where enormous amounts of money are concealed in cold storage where illegal websites and illicit commerce like ATM/ Debit/ Credit Card scams subjecting to illegal transactions rule over the deep net by utilizing the innocent public money. Case Presentation: The present study involves a case study where it was noted that innumerable ATM Debit/Credit Cards were skimmed and the illicit money was exchanged with this crypto-currency using an illicit website for bit coin mining and storing huge amounts of anonymous public money that was dictated by a few Nigerian Fraudsters running this racket all over the nation.
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
N.I. Indera, Ahmad Ihsan Mohd Yassin, Azlee Zabidi, Zairi Ismael Rizman
This paper presents a Multi-Layer Exogeneous Inputs (NARX) Bitcoin price forecasting model using the opening, closing, minimum and maximum past prices together with Moving Average (MA) technical indicators.
Bitcoin is the first decentralized cryptocurrency to be traded. There has been drastic increase in the price of bitcoin since 2013. Granger Causality analysis has been carried out to examine whether the price of commodities and the exchange rates helps in predicting the future price of bitcoin. For this study, the price of bitcoin, commodity prices and exchange rates have been considered from Jan 2103-Sep 2017. After the analysis it can be concluded that the price of commodities and the exchange rates does not help in predicting the future price of bitcoin. The past data of the price of bitcoin helps in predicting the future price of copper and British pound exchange rate with that of U.S dollars. Using Regression analysis, it can be determined that when the price increases by 0.0084 dollars there is one unit increase in the volume of transaction. Using variance analysis it can be observed that the price of bitcoin is more volatile compared to the price of commodities and the exchange rates.
Blockchain is being termed as the fifth disruptive innovation in computing. In simplest words, it is a distributed ledger of records that is immutable and verifiable. Since its advent in 2008, blockchain as a concept has been used in various ways. The largest impact or application is seen as a multitude of cryptocurrencies that have sprung up. However, with time, it has become clear that blockchain as a technology is likely to have an impact much wider than just the cryptocurrency domain and much deeper than simple distributed ledger storage. This detailed survey intends to bring together all the key developments so far in terms of putting blockchain to practice. While the most common adoption of blockchain is in finance and banking domain, there are experiments being conducted by many big players in various other domains. This paper will explore the various domains where blockchain has had an impact and where future implementations may be expected.
Bitcoin is a computerized digital money and exchange network, represents an essential change in financial sectors, an interesting number of customers and excellent evaluation of channel inspection. In this research, dataset related to ten cryptocurrencies are used and created a new dataset by taking the closing price of each cryptocurrency for the research goal to ascertain how the direction and accuracy of price of the Bitcoin can be predicted by using data mining methods. Features engineering evaluated that all the ten cryptocurrencies are strongly correlated with each other. The task is achieved by implementation of supervised learning method in which random forest, support vector classifier, gradient boosting classifier, and neural network classifier are used under classification category and linear regression, recurrent neural network, gradient boosting regressor are used under regression category. In the classification category, support vector classifier achieved the highest accuracy of 62.31% and precision value 0.77. In regression category, gradient boosting regressor got the highest R-squared value 0.99.
A comparative study across the most widely known blockchain technologies is conducted with a bottom-up approach. Blockchains are disentangled into building blocks. Each building block is then hierarchically classified in main and subcomponents. Then, alternative layouts for the subcomponents are identified and compared between them. Finally, a taxonomy tree summarises the study and provides a navigation tool across different blockchain architectural configurations.
Distributed ledger technology, a method of storing and maintaining the integrity of multiple copies of critical data using a massively redundant network of participating machines, has found a “killer application” in blockchain, a type of distributed ledger. A blockchain consists of sequential blocks that may never be modified or reordered, leaving a public, auditable record that is consistent and highly resistant to tampering and deletion. These qualities make blockchain eminently suitable for its most common use, cryptocurrency, and its occasional variants in the form of cryptocurrency tokens, used to represent ownership or some other right to virtual or physical goods and capabilities. Blockchain also enables smart contracts, discrete bodies of software written to serve both as the memorial and the means of execution of an agreement between parties. Smart contracts can have all the elements of a traditional contract, and as jurisdictions legislate or jurists rule on the fine points of enforceability and the acceptability of smart contracts as traditional contracts, applications in nearly every area of commerce have emerged. Digital lawyers may not need to become software developers, but deepening their understanding of the capabilities and limitations of the technology, developing a keen awareness of the issues at the intersection between code and the law, as well as the law’s readiness in this area, will be of great advantage to them and their clients in this rapidly evolving area at the intersection of technology, commerce and law.
Crptocurrency is a digital or virtual currency that uses cryptography for security, transfer process and storage in ledger.This paper is to validate the correlation between exchange rate changes and trading volume changes.Data selected for this study is hourly data starting from 4 November 2017 until 7 November 2017.Methodology implemented in this study started with normality diagnostics and followed by correlation diagnostic.In this study, Pearson correlation calculation is implemented to evaluate the association between two variables namely exchange rate and trading volume.Pearson's correlation coefficient (r) is a measure of the strength of the association between the two variables.Result shows the coefficient of association is 0.123.Therefore, this study proved that the association between exchange rate changes and trading volume changes is very weak association.This value occurred because there is high volatility in hourly data and existence of outliers.The significant of this finding will help investors to recognize the relationship between trading volume and exchange rate.Therefore, it will help investors to make better decision in developing investment portfolio.
Feroz Ahmad Ahmad, Prashant Kumar, Gulshan Shrivastava, Med Salim Bouhlel
ON 12 JANUARY 2009 a pseudonymous entity signed a transaction that instructed a distributed network to transfer a small amount of digital currency to Hal Finney, one ofthe key figures of the cypherpunk movement. After a few minutes, the transaction was recorded on a distributed public ledger, permanently updating the balance ofbothparties. This transaction— the first Bitcoin transaction—marked the beginning of a new era of decentralized payment systems, ushering in a variety of financial Services that do not depend on any centralized clearinghouse or other financial middleman. Bitcoin is regarded by many as a powerful technological innovation that could disrupt many sectors, in the realm of finance and beyond. But the underlying technology on which the network operates, the Bitcoin blockchain can do much more than that. Just as the internet did in the early-1990s, blockchain technology carries with it a whole new range of promises concerning how decentralization can support and promote individual freedoms and autonomy. Blockchain proponents believe that Bitcoin and other cryptocurrency platforms will revolutionize mechanisms of value exchange in the same way that the internet transformed information sharing, by providing a platform for people to exchange digital resources, in a secure and decentralized manner without the need to rely on any intermediary or trusted authority. But this revolutionary potential also carries with it serious implications for censorship, intellectual property, and the regulated flow of information. A blockchain is a decentralized database of transactions maintained by a distributed network of computers, which all contribute to the verification and the validation of transactions. Once accepted, these transactions are recorded inside a “block” of transactions, which incorporates a reference to previous blocks. This creates a long chain of blocks—a “blockchain”—that stores the history of all transactions in a chronological order. Every block contains information about a particular set of transactions, a reference to the preceding block in the blockchain, and the answer to a complex mathematical puzzle that is used to validate the data associated with that block. A copy of the blockchain is stored on every computer in the network, making it virtually impossible for anyone unilaterally to modify the data stored on this decentralized database: if anyone tries to modify any transaction the fraud will be immediately detected by all other network participants.
Open access
43 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
A short, 30cm, test section was used to study the effect of electrohydrodynamic (EHD) forces on flow redistribution in a horizontal, shell and tube heat exchanger subject to both boiling and condensation. The use of a short test section allows for a consistent flow pattern across the test section length which provides further insight into the true effect of EHD. It was found that the voltage polarity of the applied voltages influences the flow distribution. For the current geometry studied, it was found that positive polarity voltages tend to pull liquid away from heat transfer surface and that negative voltages tended to repel more liquid toward the heat transfer surface. Using this knowledge we were able to show that positive voltages were more effective for convective condensation heat transfer enhancement, whereas negative voltages were more effective for convective boiling heat transfer enhancement. A twofold enhancement of convective boiling heat transfer was achieved for positive voltages and a 4fold enhancement was achieved for negative voltages. Similar pressure drop penalties were seen for both cases, approximately twice that of the no EHD case. Furthermore, the effect of DC level, peak to peak voltage, frequency and duty cycle waveform parameters on convective boiling enhancement were studied to explore the range of controllability for the current set of flow parameters. It was found that these various waveform parameters can induce different flow patterns and consequently different heat transfer and pressure drop configurations. In general the heat transfer is enhanced by EHD, but different pressure drop penalties can be achieved for a given enhancement ratio using different waveforms. High heat transfer for relatively low pressure drop was achieved using either negative DC signals or 50%duty cycle pulse waveforms. In some cases the enhancement is quite little compared to the pressure drop, for example the zero DC level, varying peak to peak voltage data. It is suggested that in a system where the heat exchanger pressure drop due to EHD is more dominant than the system pressure drop, it may be possible to use EHD as a method of retarding the system rather than enhancing it thereby broadening the scope of controllability. Finally we showed the proof of concept of using DC EHD as a rapid control mechanism for the load conditions. Using -8kVDC the water side heat flux could be varied by approximately ±3.2 kW/m<sup>2</sup> within 5 seconds. As a comparison, the same experiment was repeated using the refrigerant flow rate to control the load. Response times were similar for both experiments and although the power required for the flow rate control was less, the minimal variability in flow parameters for the EHD control make it a more attractive method of load control.
Open access
Membrane-based Ion Separation Techniques
Currency Recognition and Detection
Innovative Microfluidic and Catalytic Techniques Innovation