Phishing Scams $225 Million Lost As Cryptocurrency by Investors
Cryptocurrency Phishing Scams
Would-be cryptocurrency investors have been conned out of a combined $225 mln cryptocurrency Phishing Scams this year alone, according to a report released by C analysis, a provider of anti-money laundering software for Bitcoin.
The hype surrounding cryptocurrencies has led to phishing attacks by cybercriminals increasing exponentially alongside the rise in ICO’s and digital token sales.
These phishing attacks relate specifically to the ICO process and fool investors by redirecting them to a mirrored social media or web page that replicates the official ICO. Investors would then hand over their payment credentials, not knowing that, instead of investing in a new digital currency, they are funding yet another cyber-criminal.
The result is that close to 30,000 individuals have lost on average $7,500 each and there’s about a one in ten chance of investors being redirected to fake ICO sites.
The reason ordinarily web-savvy individuals fall for scams like these may lie in the time-sensitive nature of ICO’s. The limited opportunity within token sale events means investors tend to rush into early access trades, making them easy targets for phishing attacks.
Although the most common, phishing is not the only tactic used by criminals to scam investors out of money. Back in 2016, a vulnerability in the DAO smart contract allowed hackers to steal $74 mln from 11,000 individuals.
The good news is that the capabilities of developers to write more secure smart contracts are increasing and is reducing the frequency of theft through scams and system manipulation.
The public landscape of the Blockchain also means that third parties are able to monitor trends in cryptocurrency usage. The data analysis can then be used to implement solutions that will safeguard these assets against malicious attacks.
For cryptocurrency networks to continue to grow and remain sustainable, it is essential for solutions to be able to observe and glean information from specific networks.