Machine Learning-Based Client-Side Protection Against Phishing and Web Spoofing

ABSTRACT Cyber security confronts a tremendous challenge of maintaining the confidentiality and integrity of user’s private information such as password and PIN code. Billions of users are exposed daily to fake login pages requesting secret information. There are many ways to trick a user to visit a web page such as, phishing mails, tempting advertisements, click-jacking, malware, SQL injection, session hijacking, man-in-the-middle, denial of service and cross-site scripting attacks. Web spoofing or phishing is an electronic trick in which the attacker constructs a malicious copy of a legitimate web page and request users’ private information such as password. To counter such exploits, researchers have proposed several security strategies but they face latency and accuracy issues. To overcome such issues, we propose and develop client-side defense mechanism based on machine learning techniques to detect spoofed web pages and protect users from phishing attacks. As a proof of concept, a Google Chrome extension dubbed as Phish Catcher, is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful. The algorithm takes four different types of web features as input and then random forest classifier decides whether a login web page is spoofed or not. To assess the accuracy and precision of the extension, multiple experiments were carried on real web applications. Keywords: Cybersecurity, Phishing Detection, Web Spoofing, Client-Side Defense, Machine Learning, Random Forest Classifier, URL Classification, Chrome Extension.