Identification of Phishing Attacks using Machine Learning
Nikhil Jindal, Dhruv Rastogi, Kartik Joshi, Deepak P. Gupta · 2023
The Internet universal connectivity is both a boon and a breeding ground for phishing attacks, manipulating unsuspecting users into interacting with harmful links that redirect them to deceptive websites aiming to pilfer personal information. Phishing, a prevalent cyber threat, often uses cleverly disguised links in emails, texts, or social media that appear authentic, leading individuals to divulge sensitive details. Moreover, the unprecedented rise of artificial intelligence in recent years has also fueled a fresh wave of phishing attacks. These have become even more sophisticated, dangerous and prevalent. Therefore, there is a need to deploy advanced solutions that can automatically detect and prevent such phishing attacks. In this paper, the proposed solution analyzes URLs, considering factors like domain name, URL length, and the presence of suspicious keywords, seeking to differentiate between legitimate and phishing attempts. Further, various machine learning models are deployed to detect and classify phishing attacks to achieve an accuracy of 95.2%.