Phishing Website Detection System Using Machine Learning
MD Arif Khan · Journal of Networking and Communication Systems (JNACS) · 2024
Phishing, categorized as a Social Engineering Attack, poses a prevalent security threat by deceptively extracting private and confidential information from users without their awareness.This sensitive data encompasses usernames, passwords, account numbers, and more.To encounter this, we proposed a website equipped with a Machine Learning (ML) Algorithm to empower users in identifying phishing websites.The effectiveness of this approach is particularly notable when applied to extensive datasets, addressing limitations present in current methodologies and enabling the detection of zero-day attacks.While ML-based classifiers generally demonstrate optimal accuracy and performance, their efficacy is contingent upon factors such as the scale of training data, feature set, and classifier type.Notably, these classifiers may fall short in detecting instances where attackers employ compromised domains for hosting their sites.Despite the absence of a foolproof system for identifying all phishing websites, adopting these methods promises an efficient means of detection.