Cryptocurrency nowadays is in most demand and many people are investing in these currencies to get high returns. Its is more frequently used as it is theft proof, accessible anywhere and anytime. By using crypto currency the settlement of money is instant. Many websites and applications provide us information about the rates of different crypto currencies available in the Crypto Market. So we will be building an application in which we will be displaying the real time rates of different crypto currencies inside our application.
In recent years, ransomware attacks have become increasingly rampant, resulting in many large companies or financial institutions suffering heavy losses from ransomware attacks. Bitcoin, is a means of payment demanded by the Ransomware Family. By comparing and analyzing the characteristics of bitcoin transactions, we can predict the types of Ransomware Family. Therefore, in this paper, the algorithm of machine learning is used to put forward the prediction method of Ransomware Family, so as to achieve the better effect of helping the attacked institutions to avoid being extorted effectively. In the traditional method, the judgment of Ransomware Family can only rely on human experience and subjective judgment, instead of accurate and batch analysis of Bitcoin transactions and prediction results. In this paper, a large number of known data sets of bitcoin's transaction features are used for analysis and modeling. First, we carried out descriptive statistical analysis to explore the differences between different Ransomware Families in bitcoin trading behavior. Next, we used a series of machine learning models to build the prediction model of Ransomware Family and conduct identification and classification, so as to help avoid financial losses from the Ransomware. Finally, we found that Ransomware family species were most significantly affected by year. In addition, it can be found that the accuracy of the Boosting model is the highest, and the test error is only about 3%.
The goal of the thesis is to identify factors that drive the price of Bitcoin and the hashrate of the Bitcoin network, which represents the total computing power dedicated to Bitcoin mining, and to explore the relationship between these two variables. In the Bitcoin system, four variables were assumed to be endogenous, thus for each of them, an equation was constructed. This was the case of the Bitcoin price, the hashrate of the Bitcoin network, the total transaction fees paid, and the search volume for the term "bitcoin". The system of four equations was then simultaneously estimated, utilizing the method of Two-stage least squares. Results revealed several statistically significant explanatory variables of the price and the hashrate, including the money supply of the United States dollar or the number of unique active addresses on the Bitcoin network. The hashrate was shown to drive the price positively, however, the estimated effect of the price on the hashrate was statistically insignificant. It was argued that it might have been caused by exogenous shocks affecting multiple variables, that could not be accounted for in the data. In addition, the factors affecting the hashrate were assessed from the environmental point of view, as the high environmental impact is one of the main points in the criticism...
Since its inception, the cryptocurrency's exceptional growth has put financial institutions at high risk of exposure to money laundering. In financial institutions, specifically banks, Anti-Money Laundering and Bank Secrecy Act (AML/BSA) risk specialists, bank managers, and compliance officers get challenged in identifying cryptocurrency-related transactions and customers who conceal illegal funds. Interviews conducted with the AML/BSA risk specialists, bank managers, and compliance officers were analyzed to understand how banks combat the cryptocurrency-related money laundering in the USA banking system. Interview with the Director of Financial Investigations & Education at CipherTrace as an expert in blockchain forensics was evaluated to recognize bank regulation and compliance. The case studies were assessed to understand the banks' program and regulation deficiencies and their inability to identify suspicious accounts. Interviews and case studies findings suggest that cryptocurrency-related money laundering is a risk for banks who lack proper tools, programs, and adequate well-trained and well-educated staff in mitigating cryptocurrency-related risks. Support provided by FinCEN regulation and guidance and external vendors is seen as critically valuable in assisting banks to combat cryptocurrency-related money laundering financial crimes.
Artificial intelligence-driven cryptocurrencies are cryptocurrencies created by Artificial intelligence using the traditional human cryptocurrency development framework without human intervention. An AI explores the data from each different stream and arriving at the framework which can host these cryptocurrencies following the standards of legality. Cryptography is the encryption of specific data to conceal it and keep it a secret from unwanted third parties. Cryptocurrencies are encrypted currencies with unique keys as developed by developers. Artificial intelligence is an advanced machine programmed to simulate and emulate human intelligence by carrying tasks and reaching conclusions with little or no human intervention. This work considered the use of AI through machine learning and deep learning in the development of cryptocurrencies. The AI machine will set all the parameters and structure of the cryptocurrency. This will include how data is added, removed, and verified on the stream. Blockchain is an open ledger of a cryptocurrency's transactions. It stores files in the system, arranged in blocks, and connected on a list called chains. The article considers how AI-driven cryptocurrency will run using the blockchain network and its impact on it. Artificial intelligence and cryptocurrency are technological very essential technological development currently. The effect of the combination of both technologies would be enormous in the future as both technologies will develop each other remarkably.
Roxana Martínez, Rocío Andrea Rodríguez, Pablo Martín Vera
This paper analyzes the quality of the data in the data sets presented in the open portal sites of the Argentine Republic, considering the portal of the Autonomous City of Buenos Aires (C.A.B.A) and the decentralized organizations of the country. The objective is to show results compared to the analysis of the data sets published in the open portals, with some important considerations when maintaining, managing and reusing systems, in order to generate added value to the community. Finally, the open data catalogs are validated, using a Python library (pydatajson), to verify if they comply with a standard catalog structure, proposed by the government of the Argentine Republic.
In every direction, there is a lot of noise about the Internet of Things (IoT) and its impact on everything. The technology of IoT is a huge network of interrelated devices and human beings that record and transmit the data to each other about the way they are used and about their surroundings. Conventional networks of IoT rely on a concentrated structure with finite scalability among other negative aspects. Hence, blockchain can deal with the IoT by providing many benefits and security to the data of the network. Globally, with the growth of technologies, companies and organizations are relying upon their data systems. Issues about missing or robbing of data are becoming a constant in news headlines because organizations depend more and more upon their computer systems to collect confidential information of customers. Therefore, in this chapter, the detailed background of IoT is introduced. Then, the problems of IoT are illustrated where blockchain can act as a rescue for security issues of IoT. Furthermore, the blockchain is described in detail with an introduction to its architecture, major features, approaches of data secrecy, and mining process. Moreover, an idea of IoT based on blockchain with applications, security, and confidentiality is described, and finally, disputes of blockchain are illustrated. The main motive of this chapter is to focus on the open research issues and directions of possible upcoming research on blockchain for IoT, as well as on the services of security and confidentiality for data using blockchain.
Basetty Mallikarjuna, T. V. Ramana, Suresh Kallam, Rizwan Patan · 5 authors
Bitcoin symbolizes cryptocurrency following peer-to-peer payment model. Here, the transactions are said to take place between pseudo-anonymous users, without any centralized authority. In other words, Bitcoin refers to digital currency with transactions being stored into a public ledger, called blockchain. Among the different types of entities, Bitcoin exchange differentiates by including a governing trading framework where most habitual clients barter fiat currency for Bitcoins and vice versa. Certain open standards are followed to ensure transparency in Bitcoins. Some of them are, transacting through public ledger, integrity being achieved via cryptographic mechanisms and privacy being attained by correlating Bitcoin owners with opaque cryptographic identifiers hiding the actual identities behind them. The blooming interest in Bitcoin has resulted to emergence of hundreds of cryptocurrency exchanges of differing sizes across the globe since its inception. Hence, Bitcoin and cryptocurrencies with huge volumes of data present several challenges. Visualization, on the other hand, empowers clients to utilize the visual senses [12] and intuition with respect to Bitcoin blockchain data. Visualization is considered to be an important area which is used in different domains and fields, to name a few are, healthcare, security, privacy, image processing and so on that provide clients with visual information[11]. The technology behind blockchain became popular with the existence of Bitcoin ecosystem [26]. Unique opportunities are being provided here to store the digital transaction history according to consensus that is publicly available to everyone. This publicly available digital transaction history in blockchain provides the probability in analyzing both the current and previous cash flows. Different visualization approaches are split into three types based on the objectives and applications, ranging from, economic visualizations to transaction visualizations and security visualizations [19–22]. Besides, visualization are classified into three different areas, where huge data pertaining to abstract data are viewed (i.e., information visualization), forming analytical reasoning (i.e., visual analytics), and information pertaining to scientific aspects (i.e., scientific visualization). Within the Bitcoin network, different types of protocol compatible data structures are generated throughout the peer-to-peer network using several algorithms. With these compatible data structures, Bitcoin system generate, transmit, establish and record corresponding data structures called as transactions. The transaction refers to the atomic record wherein the ownership of an amount of Bitcoin is conveyed by the existing client to the new client. The transactions in turn are broadcasted across the globe and each client obtains a copy of valid transactions in a data structure kept in volatile memory also referred to as the mempool or memory pool. The peer visualization aims in simply rotating globe visualization, or in other words that demonstrates the global scope of peer-to-peer network. As far as peer visualization is concerned, not only the requirement of network topology comes into existence to provide robustness but also ensure to determine the nodes possessing advantage over other in terms of system feasibility. Therefore, visualizations employed in transaction data have already grown in several contexts, to name a few being blockchain data visualization, dynamic graph visualization, and so on. Finally, to measure the efficiency and effectiveness of the Bitcoin visualization, several methods are used and conducted over different number of clients, ranging from, general public to executives from companies, researchers in various fields and so on. Novel visualization models for obtaining dynamic patterns in real time Bitcoin transaction can obtain individual transactions in a more reliable manner. Besides, meaningful associations are also found to be detected between huge transactions in analyzing fraudulent activities. The effectiveness of Bitcoin visualization remains in recognizing both the memory visualization and peer visualization, as representing all global transactions, rather than a limited subset. With the aid of these two peer and memory visualizations, understandability linking between transactions found to be more precise than the raw data.
As manufacturing, operations and maintenance become increasingly complex in the aviation sector, a digital transformation is underway toward Blockchain technology, an open source digital architecture for related data and their histories. From maintenance, repair and overhaul to protection against global positioning system spoofing, Blockchain technology is making a major impact in aviation industry. This paper analyzes possible implementation of Blockchain technology within the realm of Aviation Cybersecurity Framework.
Die Allgegenwärtigkeit des Internets macht es zu einer wertvollen Datenquelle. Produkte wie Twitter und Google Trends implementieren APIs die es erlauben diese Daten zu nützen. Da diese zwei Produkte große Mengen von Nutzerdaten speichern, kann man Sie nützen um Vorhersagen über das Benutzerverhalten in bestimmten Bereichen zu machen. In dieser Arbeit, beschreiben und implementieren wir statistische Method um Vorhersagen über den Bitcoin Wechselkurs mittels Stimmungsanalyse, der Frequenz von Tweets über Bitcoin und dem Interessenniveau zum Suchbegriff Bitcoin. Weiters zeigen wir wie das stündliche Handelsvolumen von Bitcoin mittels der stündlichen Rate von Tweets über Bitcoin und Google Trends Daten vorhergesagt werden kann. In dieser Arbeit analysieren wir Daten aus dem Zeitraum Juli 2018 bis August 2018, d.h. zwei Monate. Erstens benützen wir die stündlichen Informationen über Tweet Stimmung, Tweet Frequenz und Google Suchdaten über Bitcoin um eine kausale Verbindung zwischen diesen Daten und dem aktuellen Preis von Bitcoin. Zweitens entwickeln wir ein vektorautoregressives Modell (VAR) dass erfolgreich den Bitcoin Wechselkurs und das Bitcoin Handelsvolumen vorhersagt. Weiters, verwenden wir zwei unterschiedliche Algorithmen, SentiStrength and Stanford Core NLP, um Stimmungsinformation zu erhalten. Wir zeigen dass mit dem SentiStrength Algorithmus welcher für kurze Nachrichten wie Tweets geeignet ist unser Modell bessere Resultate erzielt. Spezifisch, zeigen wir dass unser Modell wenn kombiniert mit SentiStrength eine Genauigkeit von 63% für Vorhersagen bezüglich dem Anstieg und Abstieg des Bitcoin Wechselkurses aufweist. Auf der anderen Seite, wenn man unser Modell mit Stanford Core NLP kombiniert erhalten wir eine Genauigkeit von 59%. Drittens, benutzen wir die Tweet-Frequenz und Suchinformationen um das Bitcoin Handelsvolumen mit einem mittleren quadratischen Fehler von 172.76 vorherzusagen. Zusammenfassend, zeigen wir in dieser Arbeit dass Informationen von Twitter und Google Trends sehr hilfreich sind wenn es um die Vorhersage zwei wichtiger Bitcoin Eigenschaften, Wechselkurs und Handelsvolumen, geht.
In econophysics, statistical-physics techniques are used to model economical systems. In this thesis, we investigate the entropy and the Computational Information Density (CID) of the Bitcoin blockchain. The CID is defined as the compression ratio of some particular algorithm when applied to the raw data of the state of the system. It is related to entropy as both CID and entropy are measures of information.\nWe find a strong correspondence between the CID and entropy for the Bitcoin blockchain, where features are similar, but without one being a clear function of the other. This can be explained by intercorrelations between one agent and the next, which the entropy does not count. We also calculate some correlations to see if the CID and the entropy have some predictive power for the price, and we find a small correlation, but very small in comparison to the predictive power of the price itself.\nThese results the power of the CID-entropy correspondence and how the Bitcoin blockchain may be used as a useful large-scale toy model for econophysics. We anticipate that these results can be used for a further look into the CID-entropy relation, as the similarities are visible but there is no exact correspondence. Besides this, these results can form a basis for a further look into the predictive power of the CID or the entropy for the price.
E-voting is the process of conducting the voting process through online. The voters can cast their votes from different locations and these votes are collected and recorded electronically. Therefore, there is a need for a system to provide control and security to the whole procedure. Blockchain, a distributed ledger technology can be integrated to provide a decentralized system. Blockchain uses distributed ledger technology (DLT) to avoid forged voting option & non-repudiation and one time login of the user is also ensured. By integrating the above techniques, a secure user authentication for e-voting supported blockchain in p2p network is projected. This system would increase the safety by avoiding the forgery of votes. Similarly, the blockchain are often integrated to a spread of voting situations and a few alternative applications.
Open access
2 source records
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
The characteristics of blockchain as decentralization, transparency, business activity undeniable proof mechanism etc. have achieved extensive attention from the academic circles and industrial circles. In view of the current deficiency of poor data sharing in G2B system, data authenticity, data security, and transaction subject identity's confidentiality cannot be effectively guaranteed, and the lack of authentication for government management departments (organization institutions) providing service or implementing management to enterprise businesses, this paper proposed to construct G2B system based on blockchain. Based on maintaining the architecture of traditional G2B system and the serviced or managed characteristics of enterprise businesses, was constructed respective G2B system based on blockchain for each section of enterprise business process. Each G2B system was based on blockchain correlated by the virtual links of enterprises and serviced or managed data for enterprises' business, and constituted blockchain interconnection network. The protocol was designed and the characteristics of G2B system analyzed based on blockchain. Application mode for G2B system was designed based on blockchain. A case based on blockchain was designed, including business operation principle, consensus mechanism, and supervision to government (organization).
A protocol for enabling smart, social currency for publishers and content businesses across the internet 1 Delegated Proof of Stake Position Paper.Grigg, 2017.https://steemit
Big cities are in a netherland of government. While all types of governments face these challenges, big cities furnish an intensity and complexity that set them apart. Intensity and proximity mark the operations of urban bureaucracies. Big city mayors are often said to be ambassadors, and this role is now duplicated by subordinates. Managers represent their cities before metropolitan planning organizations in order to obtain federal highway funding. Urban competition grows more fierce and is sustained by other complementary factors. Cities not only deal with standard problems, but they must become instruments of innovation, entrepreneurship, and economic development. Underlying pressures continue and there is little big city managers can do to eliminate them. Interlocal competition, corporate mobility, and globalism make up the larger environment and are beyond the bounds of local control. Big city finance managers can also be entrepreneurial by strategic investments. The new paradigm also promotes an incremental and decentralized approach to management.
David Froelicher, Juan Ramón Troncoso-Pastoriza, João M. C. Sousa, Jean‐Pierre Hubaux
Data sharing has become of primary importance in many domains such as\nbig-data analytics, economics and medical research, but remains difficult to\nachieve when the data are sensitive. In fact, sharing personal information\nrequires individuals' unconditional consent or is often simply forbidden for\nprivacy and security reasons. In this paper, we propose Drynx, a decentralized\nsystem for privacy-conscious statistical analysis on distributed datasets.\nDrynx relies on a set of computing nodes to enable the computation of\nstatistics such as standard deviation or extrema, and the training and\nevaluation of machine-learning models on sensitive and distributed data. To\nensure data confidentiality and the privacy of the data providers, Drynx\ncombines interactive protocols, homomorphic encryption, zero-knowledge proofs\nof correctness, and differential privacy. It enables an efficient and\ndecentralized verification of the input data and of all the system's\ncomputations thus provides auditability in a strong adversarial model in which\nno entity has to be individually trusted. Drynx is highly modular, dynamic and\nparallelizable. Our evaluation shows that it enables the training of a logistic\nregression model on a dataset (12 features and 600,000 records) distributed\namong 12 data providers in less than 2 seconds. The computations are\ndistributed among 6 computing nodes, and Drynx enables the verification of the\nquery execution's correctness in less than 22 seconds.\n
This article examines the bitcoin, at present the most popular cryptocurrency. The bitcoin grew on the major pillars of the neoliberal market economy, such as liberalization, deregulation and privatization. But in the end, it turned out to be a cure for the dysfunctions of the financial system, which was based on neoliberal assumptions. The difficulty in capturing the character and status of the bitcoin still makes it elusive for the existing rules of law. Some governments observe the evolution of the bitcoin market with interest; others try to work against it. All of this makes the bitcoin an intriguing subject for research.The aim of this article is to present the original assumptions of the bitcoin system; trace the reactions to the bitcoin’s emergence in virtual reality, and next on the very real financial market; and analyze the reinterpretation of the idea that underlies the creation of the cryptocurrency. This article attempts to assess the bitcoin’s potential of achieving a seemingly impregnable position on the global financial market.
Current research has led to a rejection of the hypothesis of a normal distribution of financial assets returns. Under these conditions, portfolio variance cannot serve as a good risk measure. In this paper analyzed the daily returns of the most common cryptocurrencies: Bitcoin, Bitcoin Cash, Litecoin, XRP, Ethereum, NEM. It is shown that the asset returns are not normally distributed, but with good precision follow the Cauchy distribution. The analytical expressions for risk measure were obtained using the Cauchy distribution function and the VaR technique. The efficient frontiers of cryptocurrencies portfolios were constructed using modified Markowitz model. The purpose of the article is to assess the risks of major cryptocurrencies and to diversify the risk of cryptocurrency investing by applying a portfolio model