Cryptocurrency Research Articles

Cyber security Vulnerabilities and Remediation Through Cloud Security Tools

The proliferation of internet usage has surged dramatically, prompting individuals and businesses to conduct myriad transactions online rather than in physical spaces. The onset of the COVID-19 pandemic has further propelled this trend. Consequently, traditional forms of crime have migrated to the digital realm alongside the widespread adoption of digital technologies such as cloud computing, the Internet of Things (IoT), social media, wireless communication, and crypto currencies, amplifying security concerns in cyberspace. Notably, cybercriminals have begun offering cyber attacks as a service, automating attacks to magnify their impact. These attackers exploit vulnerabilities across hardware, software, and communication layers, perpetrating various forms of cyber attacks including distributed denial of service (DDoS), phishing, man-in-the-middle, password, remote, privilege escalation, and malware attacks. The sophistication of these attacks renders conventional protection systems, such as firewalls, intrusion detection systems, antivirus software, and access control lists, ineffective in detection. Consequently, there is an urgent imperative to devise innovative and pragmatic solutions to thwart cyber attacks. This paper elucidates the primary drivers behind cyber attacks, surveys recent attack instances, patterns, and detection methodologies, and explores contemporary technical and non-technical strategies for preemptively identifying and mitigating attacks. Leveraging cutting-edge technologies like machine learning, deep learning, cloud platforms, big data analytics, and blockchain holds promise in combating present and future cyber threats. These technological interventions can aid in malware detection, intrusion detection, spam filtering, DNS attack classification, fraud detection, identification of covert channels, and discernment of advanced persistent threats. Nonetheless, it's crucial to acknowledge that some promising solutions, notably machine learning and deep learning, are susceptible to evasion techniques, necessitating careful consideration when formulating defenses against sophisticated cyber attacks.

Cryptocurrency Portfolio Management:A Clustering-Based Association Approach

The aim of this study is to identify crypto assets with similar characteristics and to explore the similar responses of these assets to market-priced events. This process is carried out in two stages. Cluster analysis and association analysis were applied in the research. First of all, cluster analysis was performed using the variables; the total number of active unique addresses, USD value of the current supply, fixed closing price of the asset, return on investment of the asset, total of the current supply, number of transactions, USD value of the sum of native units and 30 days volatility criteria. HK-Means algorithm and R Program were used for clustering. Then, the co-movement of crypto assets was analyzed using the FP-Growth algorithm and the WEKA program. 71 crypto assets with the highest market capitalization and meeting the research criteria were included in the research. The data used in the research covers the period of May 2021-May 2022. According to the main findings obtained from the research; within the framework of the criteria used in the research, 4 clusters were formed. Most important association rules found to be between; btc (bitcoin) & aave (nominex), eth (ethereum) & aave (nominex), dot (polkadot) & aave (nominex), neo & aave (nominex), uni (uniswap) & aave (nominex) , btg (bitcoin gold) & etc (ethereum classic), xrp (riple) & algo (algorand) & doge (dogecoin), xrp (riple) & doge (dogecoin), cro (cronos) & xrp (riple) & algo ( algorand) & trx (tron) & doge (dogecoin).

Evolution of Intangible Property to Crypto Assets: Legal Pragmatism in Anglo-American Common Law and Chinese Civil Law

Abstract The emergence of crypto assets, a novel category of assets, has posed significant challenges to property law across various legal systems. The integration of these intangible assets into property law frameworks proves to be complicated for many civil law jurisdictions, which tend to adhere to dogmatic approaches. In contrast, Anglo-American common law and Chinese civil law systems, despite their divergent origins, have exhibited legally pragmatic responses to intangible property, including crypto assets. By examining the development of their property laws concerning property conceptualization and the treatment of emerging intangible assets, this article explores how the historical foundations of Anglo-American common law in feudal tradition and Chinese civil law in communist ideologies have fostered adaptable and flexible notions of property. This adaptability is argued to be a strength within both the Anglo-American common law and Chinese civil law systems, enabling them to accommodate innovative intangible assets more easily compared to their civilian counterparts.

Nonfungible Tokens—Differentiated Crypto Assets in Need of Accounting and Attestation Guidelines; Analysis and Implications

ABSTRACT Even as recent headlines announce the risks of crypto assets, institutional and governmental adoption continues. Undeterred by short-term price declines and liquidation of speculative instruments, blockchain-based instruments and applications continue to receive billions in investment across the globe. And yet, accounting and attestation standards have failed to keep pace with this growth. This paper focuses specifically on the development of one type of crypto asset—nonfungible tokens (NFTs)—and the relevant accounting and auditing concerns. The first section reviews the nature of NFTs, differentiating them from other existing crypto assets. The second section examines factors that must be considered for the audit and attestation of NFTs. This paper raises questions that both the academician and practitioner must contend with as these crypto assets continue to be adopted.

Подходы к налогообложению операций с цифровыми финансовыми активами в иностранных юрисдикциях

The article analyses current approaches and mechanisms for taxation of transactions with crypto-assets in foreign jurisdictions, which are advanced in terms of the prevalence of crypto-assets among the population. The choice of the research object is due to the increasing role of such assets in Russia and around the world, as well as the need to develop current regulation in this area in Russia and analyse the possibility of adapting the most advanced and successful experience in Russian realities. The article presents an analysis of the current regulation in the field of taxation of transactions with crypto assets in foreign jurisdictions, reflects legislative gaps and problems faced by individuals and legal entities, and also sets out proposals as to the possibility of adapting foreign approaches in Russia. It was revealed that currently none of the analysed jurisdictions has an effective mechanism for taxation of transactions with crypto-assets, however, each of these jurisdictions has a unified approach formulated at the state level to determine the status of crypto-assets as an object of civil turnover and taxation, which allows market participants to develop this sector of the economy in the jurisdiction.

Volatility Concerns for Crypto Currency Investments in India Amid Fears of Inflation

Volatility Concerns for Crypto Currency Investments in India Amid Fears of Inflation

Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments

Cryptocurrency Market Dynamics: Trends, Volatility, and Regulatory Challenges

The research paper takes a deep dive into crypto currency market dynamics and regulation to bridge the gap of understanding these issues with implications for the economy. The arrival of crypto-currencies changes the terrain of finance completely by bringing in a new asset class with unprecedented level of volatility. Through this research paper, we will try to conduct a thorough investigation into the relationships within the crypto currency market, which will cover trend analysis, volatility patterns, and sketching the volatile regulatory status that accompanies this evolving environment. By adopting a holistic strategy including quantitative data analysis, qualitative research and regulatory watch, this paper will contribute to the knowledge about the mechanisms behind crypto currency markets and provide actionable strategies to the stakeholders enabling them to determine a clear course of action in this dynamic environment. The research is seeking to offer essential impressions about crypto currency ecosystem dynamics and regulatory pressure as a contribution to getting a bigger understanding of the sector which is emerging fast.

Cryptocurrency in India: Challenges and Opportunities

- In the present time, technological progress has forced humans to use the Internet. Apart from personal work, the use of Internet has also become mandatory in the financial sector. In the economic world, from the general public to businessmen, employees and industrialists, it is a new currency. Its name is crypto currency and it is being used for more profit. In general terms, it is said that crypto currency is a digital currency. It is used for exchange or transaction by various traders and investors through the internet. No intermediary bank, financial institution, central authority is required for its exchange. In this process, there is less cost, less time and transfer is done easily. Today, the prevalence of crypto currency is increasing rapidly in the world as well as in India. Experts believe that this currency will prove to be more popular and profitable in the future. This research paper is based on the advantages, disadvantages and possibilities of crypto currency in India, in which we will study whether the Indian government's process for it is positive or negative, etc.

Investigating and mitigating blockchain poisoning attacks

With the burgeoning adoption of smart contracts and the intrinsic anonymity that blockchain technology provides, the past few years have witnessed an exponential surge in the deployment of blockchain platforms for managing crypto assets. While Ether stands as a paragon of blockchains equipped with smart contract capabilities, its noteworthy to mention that such blockchains present a blank canvas in the form of a data list, dedicated for unbridled program storage. This vacant space, though pivotal for various operations, can become a potential Achilles heel. Specifically, if this repository gets tainted with malicious data, the repercussions can reverberate across the entire blockchain, disrupting its core functions. The research in focus shines a spotlight on a particularly insidious threat, termed blockchain poisoning attack. Through this stratagem, malevolent actors can, with minimal expense, embed detrimental files within the data space, thereby extensively polluting the blockchain. This isnt just a theoretical hazard; its practical implications are stark and can jeopardize the integrity of countless transactions. In a commendable feat of investigative rigor, the research meticulously decoded the modus operandi behind these blockchain poisoning attacks. By employing a multi-pronged research approach, encompassing in-depth studies and iterative inquiries, it successfully deconstructed the exact techniques that malefactors employ.

Rational Investing or Speculative Fever?: SPACs, Robinhood, and Digital Assets—Securities Markets or Casinos?

This article focuses a recurring theme – speculation in the financial markets. The 2010-2020 decade set the stage for a new round of speculative activity starting in 2021. In the article that follows I reflect on a new wave of speculation and three current examples of speculative activity. The article concludes that regulators should be cautious about over-regulation of SPACs and gamified trading. The article also supports the regulation of digital assets (crypto currencies and NFTs) as securities.

Taxes and social instability: How large corporations and wealthy taxpayers hide taxable money?

<p><span lang="EN-US" style="font-size: 12pt; font-family: verdana, geneva, sans-serif; color: black;">Non-compliance with tax obligations, particularly by wealthy taxpayers and large corporations, continues to represent a source of social injustice that fuels social instability, especially in developing countries. Understanding the strategies of tax evasion and avoidance is of critical importance. This article highlights five main strategies used by large corporations and wealthy taxpayers to avoid taxes, including tax havens, the underground economy, aggressive tax optimization, alternative financial markets, and crypto currencies. We also propose several actions to tackle global tax non-compliance, including prevention, peer reporting, active monitoring of compliance indicators and inter- national cooperation. These actions can be combined to achieve optimal results in reducing tax evasion and avoidance.</span></p>

Understanding Public Sentiment Towards Crypto Assets

This study aims to examine the evolution of public sentiment towards crypto assets. The research approach involves sentiment analysis methodology utilizing the SentiStrength v2.3 software. The data utilized in this study is secondary and obtained from scholarly publications indexed in the Scopus database. A total of 112 scholarly publications related to the research topic were identified. The research findings indicate that positive sentiment accounts for 27%, negative sentiment for 34%, and neutral sentiment contributes 39%. Further analysis reveals that the year 2023 marked a period with the highest number of sentiments, with positive sentiment dominating with 23 sentiments. This sentiment analysis method provides an in-depth understanding of the dynamics of public perception towards crypto assets, and these findings can offer valuable insights into the trends and shifts in sentiment that occur over time. Sentiment analysis can offer deep insights into how cryptocurrencies are perceived by the public and how these perceptions evolve in line with changes in social and economic contexts.

Impact of Crypto Assets as Risk Diversifiers: A VAR-based Analysis of Portfolio Risk Reduction

This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.

Harmonising cryptocurrency regulation in Europe: opportunities for preventing illicit transactions

This study focuses on the analysis of cryptocurrency regulatory developments in Europe. The aim is to review national crypto-legislation in Europe and the EU's latest initiative to create designated regulatory instruments for the crypto-economy. This study assessed whether the European Union's Regulation on Markets in Crypto-Assets (MiCA) would have the intended effect. Drawing on the results of a survey of crypto experts from five European countries, this study evaluated the effectiveness of current regulation across Europe and how it can be improved to reduce financial crimes. The findings show that a unified national legal framework for regulating transactions with crypto assets does not exist in European countries. Current crypto regulations are dictated by anti-money laundering recommendations. This study provides suggestions for improving MiCA regulation. The article offers recommendations for an international regulatory standard for crypto assets and insights for increasing efficiency in regulating DeFi, NFTs, and smart contracts.

Crypto Currency and Digital Coins Overtaking the Traditional Banking Sector

Digital currencies and coins are methods of computer-generated currency which uses cryptography for safety and operate individually of a dominant authority, like governments and economic institution. They are spread out and usually utilize blockchain technology to note transactions strongly. Cryptocurrencies such as Bitcoins, Ethereum, and some others have grown popularity in the latest eons for their latent to deliver borderless, secure, and fast transactions. Yet, they also arisen with threats like security concerns, regulatory uncertainty, and price volatility. It is vital for operators to conduct detailed research and comprehend the threats included before capitalizing or utilizing cryptocurrencies. Even though cryptocurrencies and coins have grown famous and are being gradually used for countless transactions, it is significant to remind that they still have not passed the traditional banking systems. Traditional banking sectors still perform an important part in the worldwide economic system, offering services like payment processing, savings accounts, and lending. Conversely, the growth of cryptocurrencies has directed to conferences about the possible influence on the financing sector and the necessity for traditional banking systems to acclimate to the varying setting of digital economics. It is important to observe these growths closely to comprehend the evolving association among the crypto-currencies and system of traditional banking. Hence, the current study gives the deep knowledge in the usage of crypto currencies, digital coins and their role in financial sectors that is dominating the traditional banking sector. And analyzed the impact of crypto currencies and digital coins on investors and economy of the nation and also regarding the easy accessibility of finance.

Maximum Likelihood Estimation of Multivariate Regime Switching Student‐t Copula Models

SummaryWe propose a multivariate regime switching model based on a Student‐ copula function with parameters controlling the strength of correlation between variables and that are governed by a latent Markov process. To estimate model parameters by maximum likelihood, we consider a two‐step procedure carried out through the Expectation–Maximisation algorithm. To address the main computational burden related to the estimation of the matrix of dependence parameters and the number of degrees of freedom of the Student‐ copula, we show a novel use of the Lagrange multipliers, which simplifies the estimation process. The simulation study shows that the estimators have good finite sample properties and the estimation procedure is computationally efficient. An application concerning log‐returns of five cryptocurrencies shows that the model permits identifying bull and bear market periods based on the intensity of the correlations between crypto assets.

Review of Literature on Taxability of Cryptocurrency

Virtual Currency, Digital Currency, Crypto Asset, DLT(Distributed Ledger Technology) , Payment tokens, Virtual Asset, Bitcoin are some of the terms commonly used to denote the crypto currency without any standard common definition. These are all types of crypto assets developed initially in 2009. Bitcoin, Ethereum are some of the widely used crypto currencies. All around the world some countries have regularised this field, some are yet to regularise. The entire event is based on the concept of decentralisation or no need of a Central organ to control or monitor such currencies. The various articles published with different ideas regarding the taxability of such cryptocurrencies are analysed .

Crypto Technology - Impact on Global Economy

The last decade has been marked by the evolution of cryptocurrencies, which have captured the interest of the public through the offered opportunities and the feeling of freedom, resulting from decentralization and lack of authority to oversee how cryptocurrency transactions are conducted. The innovation in crypto space is often compared to the impact internet had on human life. There is a new term called Web 3.0 for denoting all new computing innovations arising due to the blockchain technologies. Blockchain has emerged as one of the most important inventions of the last decade with crypto currencies or financial use case as one of the domains which progressed most in the last 10 years. It is very important to research about Web 3 technologies, how it is connected to crypto economy and what to expect in this field for the next several decades.

Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02.