This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effect...This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effectively buy and trade cryptocurrencies.While it contextualises the common queries of"why is crypto crashing?"and"why is crypto down?",the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency.Contrasting existing literature,this comprehensive investigation encompasses both the economic and cybersecurity risks inherent in the blockchain and fintech spheres.Through an interdisciplinary approach,the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse.Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies,the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies.Moreover,it probes into both enduring and dubious crypto projects,drawing a distinct line between genuine blockchain applications and Ponzi-like schemes.The conclusion resolutely affirms the staying power of blockchain technologies,underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.展开更多
Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge ...Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.展开更多
This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods.Two extensions are off...This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods.Two extensions are offered to compare the performance of the linear specification of the market model(LMM),which allows for the measurement of the cryptocurrency price beta risk.The first is the generalized additive model,which permits flexibility in the rigid shape of the linearity of the LMM.The second is the time-varying linearity specification of the LMM(Tv-LMM),which is based on the state space model form via the Kalman filter,allowing for the measurement of the time-varying beta risk of the cryptocurrency price.The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization,using the Crypto Currency Index 30(CCI30)as a market proxy and 1-day and 7-day forward predictions.Such a comparison of cryptocurrency prices has yet to be undertaken in the literature.The empirical findings favor the Tv-LMM,which outperforms the others in terms of modeling and forecasting performance.This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear,especially during the COVID-19 period.展开更多
This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology foc...This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy.Daily crypto and stock prices were obtained from the Standard&Poor’s composite 1500 energy index and CoinMarketCap between November 2017 and January 2020.This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk.Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos.Furthermore,most price violations were frequent during price falls compared to price increases,supporting that the Fibonacci instrument does not capture price movements during up and downtrends,respectively.Also,fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break.Furthermore,the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model.Finally,complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents.This study’s overall findings elucidate that,despite significant drops in oil prices,speculators(traders)can implement profitable strategies when using technical analysis indicators,like the Fibonacci retracement tool,with or without price crossover rules.展开更多
With the development of quantum computing technology,quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key cryptography.This paper designs a quantum trapdoor one-way...With the development of quantum computing technology,quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key cryptography.This paper designs a quantum trapdoor one-way function via EPR pairs and quantum measurements.Based on this,a new quantum public-key cryptosystem is presented,which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext attack.Compared with the existing quantum public-key cryptos,eavesdropping can be automatically detected in this new quantum public-key cryptosystem under a necessary condition,which is also detailed in the paper.展开更多
To keep pace with the development of the crypto industry due to its sheer size and complex business models,the Organisation for Economic Co-operation and Development(OECD)introduced the Crypto Asset Reporting Framewor...To keep pace with the development of the crypto industry due to its sheer size and complex business models,the Organisation for Economic Co-operation and Development(OECD)introduced the Crypto Asset Reporting Framework(CARF)to enhance transparency in relevant transactions.It is envisaged that the introduction of CARF will facilitate the exchange of information and potentially lead to increase in tax revenues for those jurisdictions enacting such requirements in due course.In turn,industry players will need to assess the implications this will have in their business from reporting readiness and implementation perspectives.Meanwhile,the tokenisation of real-world assets(RWAs)is gaining momentum.The legal and tax implications that may arise from this latest development can be complex and uncertain.展开更多
Significant improvements should be made to increase the market potential of crypto mining.However,it is not financially feasible to make too many improvements because all actions lead to cost increases.In this context...Significant improvements should be made to increase the market potential of crypto mining.However,it is not financially feasible to make too many improvements because all actions lead to cost increases.In this context,it is necessary to determine the factors that most affect this process.Accordingly,the purpose of this study is to understand the main indicators that can improve the market potential of crypto mining activities.Therefore,the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities.In this context,a new model has been constructed to answer this question.First,significant indicators are identified based on the evaluation of the literature.After that,these factors are weighted via quantum picture fuzzy rough set-based M-SWARA.The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of crypto mining activities.The M-SWARA model is taken into consideration for criteria weighting.Owing to this issue,the causal relationships between the items can be identified.The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry.Furthermore,technological developments also play an important role in this regard.展开更多
在勒索软件检测方法中,动态环境下的动态行为和网络行为方面存在综合行为分析不足的局限性,故提出了一种基于本地动态行为特征和网络行为特征的机器学习检测方法(machine learning detection method based on local dynamic behavior fe...在勒索软件检测方法中,动态环境下的动态行为和网络行为方面存在综合行为分析不足的局限性,故提出了一种基于本地动态行为特征和网络行为特征的机器学习检测方法(machine learning detection method based on local dynamic behavior features and network behavior features, ML-LDNB)。依据机器学习理论,首先通过分析本地动态行为,提取多维度本地动态行为特征,采用逻辑回归分类器进行勒索软件的预判;同时,在网络层面,通过分析网络流量数据包,提取关键网络行为特征,采用决策树分类器进行勒索软件的预判;最后利用多数投票算法将本地动态行为特征和网络行为特征的预判结果综合起来作为勒索软件识别依据。该方法的检测准确率达98%,充分证明了该方法的有效性和可靠性。展开更多
基金supported by the PETRAS National Centre of Excellence for IoT Systems Cybersecurity,which has been funded by the UK EPSRC[under grant number EP/S035362/1]the Software Sustainability Institute[grant number:EP/S021779/1]by the Cisco Research Centre[grant number CG1525381].
文摘This study examines blockchain technologies and their pivotal role in the evolving Metaverse,shedding light on topics such as how to invest in cryptocurrency,the mechanics behind crypto mining,and strategies to effectively buy and trade cryptocurrencies.While it contextualises the common queries of"why is crypto crashing?"and"why is crypto down?",the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency.Contrasting existing literature,this comprehensive investigation encompasses both the economic and cybersecurity risks inherent in the blockchain and fintech spheres.Through an interdisciplinary approach,the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse.Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies,the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies.Moreover,it probes into both enduring and dubious crypto projects,drawing a distinct line between genuine blockchain applications and Ponzi-like schemes.The conclusion resolutely affirms the staying power of blockchain technologies,underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.
文摘Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.
文摘This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods.Two extensions are offered to compare the performance of the linear specification of the market model(LMM),which allows for the measurement of the cryptocurrency price beta risk.The first is the generalized additive model,which permits flexibility in the rigid shape of the linearity of the LMM.The second is the time-varying linearity specification of the LMM(Tv-LMM),which is based on the state space model form via the Kalman filter,allowing for the measurement of the time-varying beta risk of the cryptocurrency price.The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization,using the Crypto Currency Index 30(CCI30)as a market proxy and 1-day and 7-day forward predictions.Such a comparison of cryptocurrency prices has yet to be undertaken in the literature.The empirical findings favor the Tv-LMM,which outperforms the others in terms of modeling and forecasting performance.This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear,especially during the COVID-19 period.
文摘This paper investigates the role of Fibonacci retracements levels,a popular technical analysis indicator,in predicting stock prices of leading U.S.energy companies and energy cryptocurrencies.The study methodology focuses on applying Fibonacci retracements as a system compared with the buy-and-hold strategy.Daily crypto and stock prices were obtained from the Standard&Poor’s composite 1500 energy index and CoinMarketCap between November 2017 and January 2020.This study also examined if the combined Fibonacci retracements and the price crossover strategy result in a higher return per unit of risk.Our findings revealed that Fibonacci retracement captures energy stock price changes better than cryptos.Furthermore,most price violations were frequent during price falls compared to price increases,supporting that the Fibonacci instrument does not capture price movements during up and downtrends,respectively.Also,fewer consecutive retracement breaks were observed when the price violations were examined 3 days before the current break.Furthermore,the Fibonacci-based strategy resulted in higher returns relative to the naïve buy-and-hold model.Finally,complementing Fibonacci with the price cross strategy did not improve the results and led to fewer or no trades for some constituents.This study’s overall findings elucidate that,despite significant drops in oil prices,speculators(traders)can implement profitable strategies when using technical analysis indicators,like the Fibonacci retracement tool,with or without price crossover rules.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0309702)the National Natural Science Foundation of China(Grant No.61502526)+1 种基金the National Safety Academic Fund(Grant No.U2130205)the Natural Science Foundation of Henan(Grant No.202300410532)。
文摘With the development of quantum computing technology,quantum public-key cryptography is gradually becoming an alternative to the existing classical public-key cryptography.This paper designs a quantum trapdoor one-way function via EPR pairs and quantum measurements.Based on this,a new quantum public-key cryptosystem is presented,which offers forward security,and can resist the chosen-plaintext attack and chosen-ciphertext attack.Compared with the existing quantum public-key cryptos,eavesdropping can be automatically detected in this new quantum public-key cryptosystem under a necessary condition,which is also detailed in the paper.
文摘To keep pace with the development of the crypto industry due to its sheer size and complex business models,the Organisation for Economic Co-operation and Development(OECD)introduced the Crypto Asset Reporting Framework(CARF)to enhance transparency in relevant transactions.It is envisaged that the introduction of CARF will facilitate the exchange of information and potentially lead to increase in tax revenues for those jurisdictions enacting such requirements in due course.In turn,industry players will need to assess the implications this will have in their business from reporting readiness and implementation perspectives.Meanwhile,the tokenisation of real-world assets(RWAs)is gaining momentum.The legal and tax implications that may arise from this latest development can be complex and uncertain.
文摘Significant improvements should be made to increase the market potential of crypto mining.However,it is not financially feasible to make too many improvements because all actions lead to cost increases.In this context,it is necessary to determine the factors that most affect this process.Accordingly,the purpose of this study is to understand the main indicators that can improve the market potential of crypto mining activities.Therefore,the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities.In this context,a new model has been constructed to answer this question.First,significant indicators are identified based on the evaluation of the literature.After that,these factors are weighted via quantum picture fuzzy rough set-based M-SWARA.The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of crypto mining activities.The M-SWARA model is taken into consideration for criteria weighting.Owing to this issue,the causal relationships between the items can be identified.The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry.Furthermore,technological developments also play an important role in this regard.
文摘在勒索软件检测方法中,动态环境下的动态行为和网络行为方面存在综合行为分析不足的局限性,故提出了一种基于本地动态行为特征和网络行为特征的机器学习检测方法(machine learning detection method based on local dynamic behavior features and network behavior features, ML-LDNB)。依据机器学习理论,首先通过分析本地动态行为,提取多维度本地动态行为特征,采用逻辑回归分类器进行勒索软件的预判;同时,在网络层面,通过分析网络流量数据包,提取关键网络行为特征,采用决策树分类器进行勒索软件的预判;最后利用多数投票算法将本地动态行为特征和网络行为特征的预判结果综合起来作为勒索软件识别依据。该方法的检测准确率达98%,充分证明了该方法的有效性和可靠性。
