TY - JOUR

T1 - PhishCatcher

T2 - Client-Side Defense Against Web Spoofing Attacks Using Machine Learning

AU - Ahmed, Muzammil

AU - Altamimi, Ahmed B.

AU - Khan, Wilayat

AU - Alsaffar, Mohammad

AU - Ahmad, Aakash

AU - Khan, Zawar Hussain

AU - Alreshidi, Abdulrahman

PY - 2023/6/19

Y1 - 2023/6/19

N2 - 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 defence 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 PhishCatcher , 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. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool, we performed experiments over forty phished URLs. The average recorded response time of PhishCatcher was just 62.5 milliseconds.

AB - 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 defence 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 PhishCatcher , 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. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool, we performed experiments over forty phished URLs. The average recorded response time of PhishCatcher was just 62.5 milliseconds.

KW - General Engineering

KW - General Materials Science

KW - General Computer Science

KW - Electrical and Electronic Engineering

U2 - 10.1109/access.2023.3287226

DO - 10.1109/access.2023.3287226

M3 - Journal article

VL - 11

SP - 61249

EP - 61263

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -