The interplay between crypto market conditions and phishing crimes: Ethereum under the microscope

This study examines the relationship between global phishing crime and cryptocurrency-market conditions, with a specific focus on Ethereum. Using monthly data from January 2016 to December 2022, we analyse the returns of global phishing crime numbers together with six Ethereum financial metrics relating to transactions, trading volume, and price impact. We employ quantile regression, quantile-on-quantile regression, and Granger causality in quantiles to examine whether the relationship between Ethereum market indicators and phishing activity varies across different market states. The results reveal a state-dependent relationship. Large increases in phishing crime numbers are strongly associated with large increases in Ethereum transaction activity, average transaction price, and transaction quantity, while implicit transaction cost is predominantly negatively associated with phishing activity, particularly at the upper quantiles. These findings suggest that phishing risk is most pronounced during extreme market conditions and may be shaped by both reward-enhancing market activity and cost-enhancing transaction frictions. To interpret these patterns, we develop an incentive-based criminogenic mechanism in which Ethereum market conditions affect phishing activity through offenders’ expected payoff. We identify two mediating channels: a monetisation-frictions channel, operating through liquidity, price impact, slippage, and transaction costs; and an attention/information-asymmetry channel, operating through volatility, speculative attention, fear of missing out, and user vulnerability. The findings provide initial evidence that cryptocurrency-related phishing is not only a technical cybersecurity issue, but also a market-sensitive phenomenon shaped by financial incentives, liquidity conditions, and behavioural vulnerability. These insights can support regulators, law enforcement agencies, and cryptocurrency platforms in developing adaptive early-warning and prevention strategies.