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As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early months of the pandemic. By collecting, synthesizing, and analyzing DNS records, TLS certificates, phishing URLs, phishing website source code, phishing emails, web traffic to phishing websites, news articles, and government announcements, we track trends of phishing activity between January and May 2020 and seek to understand the key implications of the underlying trends. We find that phishing attack traffic in March and April 2020 skyrocketed up to 220% of its pre-COVID-19 rate, far exceeding typical seasonal spikes. Attackers exploited victims uncertainty and fear related to the pandemic through a variety of highly targeted scams, including emerging scam types against which current defenses are not sufficient as well as traditional phishing which outpaced the ecosystems collective response.
With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem.
In recent years, phishing scams have become the crime type with the largest money involved on Ethereum, the second-largest blockchain platform. Meanwhile, graph neural network (GNN) has shown promising performance in various node classification tasks
The prosperity of the cryptocurrency ecosystem drives the needs for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the cu
Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as
Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from