SECURING DIGITAL ECONOMIES: DETECTION OF PHISHING ATTACKS USING MACHINE LEARNING APPROACHES

Due to the wide use of web resources such as online banking, social networking, education, entertainment, downloading software, and a rise in the economy's digitalisation, the prevalence of online fraud is rising. Phishing is mostly a launch attack on the internet. Phishing is a criminal attack that obtains credential data such as bank information, username, password and credit card information, etc which can be used to damage a single victim or the entire organisation. Criminals always use disasters as an opportunity to take advantage of recessions, hurricanes, and difficult times. The latest example is the Covid-19 pandemic. The phishing attacks are growing rapidly in 2020, cyber attackers launched Covid-19 themed phishing attacks against healthcare facilities, the unemployed and workers. A lot of anti-phishing solutions are available heuristic detection, virtual similarity detection, black and whitelisting, and machine learning. This paper compares various research for each machine learning technique in terms of detecting phishing attacks and discusses the benefits and drawbacks of each methodology. In addition, this paper presents a detailed list of existing phishing attack threats as well as potential research directions in this sphere.