Machine learning approach for phishing website detection : A literature survey - Patil, Rutuja R.; Kaur, Gagandeep; Jain, Himank; Tiwari, Ayush; Joshi, Soham; Rao, Keshav; Sharma, Amit (Journal of discrete mathematical sciences & cryptography, 2022)
The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature.