Ransomware is the most predominant cyber threat in the digital infrastructure. The attackers launching ransomware attacks use different techniques to hijack the users’ or organizations’ files and ...resources to demand ransom in exchange to free the encrypted/captured data or resources. Although there are many malware attacks, ransomware is considered most dangerous as it imposes a high financial burden on the organization. The crypto-currency is an untraceable payment method that the attacker uses to receive ransom from victims to conceal his/her identity and location. This still creates challenges to trace the attacker or attackers’ networks. The article uses the systematic literature review (SLR) approach to provide significant study on the ransomware attacks as it is the area that requires top most attention in critical infrastructure. The paper briefs the various types of ransomware, vulnerabilities, attack methodologies, impacts, mitigation and prevention techniques of the attacks. This research study is mainly focused on Windows OS vulnerabilities. These findings in the survey will be highly beneficial to understand the effects of ransomware attacks in critical infrastructure environments and the use of machine learning to detect and prevent these attacks.
We introduce the distributed ledger (blockchain) technology of crypto‐currencies. We examine the ‘monetary’ attributes of crypto‐currencies, and describe some of the reasons they have been adopted. ...The paper discusses the mechanics of Bitcoin – the original crypto‐currency – to illustrate the fundamental elements of decentralized crypto‐currencies. We then provide a high‐level summary of the implications of crypto‐currencies for consumers, financial systems, and for monetary and regulatory authorities. We argue that crypto‐currencies are unlikely to supplant traditional fiat currencies and we anticipate an enduring role for financial intermediaries in facilitating credit.
This paper introduces an autonomous payment administration solution, integrating blockchain-enabled smart contracts and robotic reality capture technologies. The construction progress is captured, ...analyzed, and documented respectively using sensing, machine intelligence, and as-built building information models (BIM). The progress data is stored in a distributed manner using content addressable file sharing; it is then broadcasted to a smart contract which administers payments and transfers lien rights respectively using crypto currencies/tokens and non-fungible tokens (NFT). The method was successfully implemented for payments to 7 subcontractors in two real-world commercial construction projects in Canada and United States where progress was captured using a camera-equipped unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) equipped with a laser scanner. The method eliminates reliance on today's heavily intermediated payment applications and showed promise for achieving accurate, efficient, and timely payment administration. Future work should further explore the connections between off- and on-chain realities.
•smart contract-based method for automation and decentralization of progress payment.•Autonomous transition from product flow (reality capture) to cash flow (payment).•Real-world implementation in the context of UAV- and UGV-enabled progress monitoring.•Smart contract can provide accurate, timely, and efficient payment administration.
•An online survey examined the predictors of cryptocurrency trading.•Engagement in gambling and stock trading was positively related to crypto trading.•Problem gambling scores were positively related ...to crypto trading.
Some forms of speculative trading share similarities with gambling. Decisions are often based on limited information, short-term motives for gain, and highly volatile and uncertain outcomes. Given these similarities, there is evidence to show that people who are attracted to gambling are also statistically more likely to engage in higher risk speculation such as day-trading of stocks and crypto-currency trading. In this study, involving 543 people (M = 388, F = 155, 85% aged 18–40 years) who reported at least monthly sports-betting, crypto-currency trading or both, we examined whether gambling and problem gambling were reliable predictors of the reported intensity of crypto-currency trading. The results showed that gambling and problem gambling rates were highest among those who reported both activities and that problem gambling scores (PGSI) and engaging in stock trading was significantly related to measures of crypto-currency trading intensity as based on the time spent per day, number of trades and level of expenditure. Future research should examine whether gambling history and involvement influences how people manage their investments in crypto-currencies, including their propensity for making riskier decisions and experiencing more negative outcomes.
Persistence in the cryptocurrency market Caporale, Guglielmo Maria; Gil-Alana, Luis; Plastun, Alex
Research in international business and finance,
12/2018, Volume:
46
Journal Article
Peer reviewed
Open access
This paper examines persistence in the cryptocurrency market. Two different long-memory methods (R/S analysis and fractional integration) are used to analyse it in the case of the four main ...cryptocurrencies (BitCoin, LiteCoin, Ripple, Dash) over the sample period 2013–2017. The findings indicate that this market exhibits persistence (there is a positive correlation between its past and future values), and that its degree changes over time. Such predictability represents evidence of market inefficiency: trend trading strategies can be used to generate abnormal profits in the cryptocurrency market.
The penetration of renewable resources and electric vehicles has increased in recent years due to various benefits such as reducing environmental pollution. This paper optimizes the energy management ...of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources and crypto-currency miners as an emerging critical load with high energy consumption. In such structures, the fluctuation of renewable energies decreases reliability and increases energy market trading. Hence, the conditional value-at-risk index is utilized to analyze the risk of uncertainties. Furthermore, the influence of responsive local loads and incoming vehicles into the stations is investigated using demand response programs. In order to implement the presented programming, a real distribution network in Kermanshah, Iran, is selected as the case study. The results show that the risk-averse strategy with α = 0.85 and β = 0.8, reduces the expected revenue by about 636.355 $ compared to the risk-neutral strategy. In addition, the swapping station can be used as a bulk storage, where it stores about 43.56 MWh in low-price times and injects about 20.04 MWh in high-price times to support the local system. The demand side management also increases the revenue even under the worst-case conditions by about 79.623 $.
•Planning a real microgrid integrated with battery charging and swapping stations.•Considering crypto-currency miners as a new type of electrical energy consumers.•Implementing demand side management programs for local loads and both stations.•Modeling the uncertainty of energy/crypto-currency markets and renewable energies.•Utilizing conditional-value-at-risk index to measure the risk of uncertainties.
The creation of bitcoin heralded the arrival of digital or crypto-currency and has been regarded as a phenomenon. Since its introduction, it has experienced a meteoric rise in price and rapid growth ...accompanied by huge volatility swings, and also attracted plenty of controversies which even involved law enforcement agencies. Hence, claims abound that bitcoin has been characterized by bubbles ready to burst any time (e.g. the recent collapse of bitcoin's biggest exchange, Mt Gox). This has earned plenty of coverage in the media but surprisingly not in the academic literature. We therefore fill this knowledge gap. We conduct an econometric investigation of the existence of bubbles in the bitcoin market based on a recently developed technique that is robust in detecting bubbles - that of Phillips et al. (2013a). Over the period 2010-2014, we detected a number of short-lived bubbles; most importantly, we found three huge bubbles in the latter part of the period 2011-2013 lasting from 66 to 106 days, with the last and biggest one being the one that 'broke the camel's back' - the demise of the Mt Gox exchange.
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