LINKCALCULATOR – AN EFFICIENT LINK-BASED PHISHING DETECTION TOOL

Author:

Abiodun Orunsolu,A.S Sodiya,S.O Kareem

Abstract

The problem of phishing attacks continues to demand new solutions as existing solutions are limited by various challenges such as high computational requirements, zero-day attacks, needs for updates, complex ruled-based, etc. Besides, the emerging mobile market demands simple solutions to phishing due to several factors such as memory, fragmentation, etc. In response to the above challenges, a simple anti-phishing tool called LinkCalculator is presented. The proposed LinkCalculator anti-phishing scheme is based on an algorithm designed to extract link characteristics from loading URLs to determine their legitimacy. Unlike the other link-based extraction approaches, the proposed approach introduced the concept of weight to represent the different links found in a URL. This is because certain link information within parsed webpages or requests is sufficient to classify them as phishing without loss of generality. The approach is experimented using a dataset of 300 instances consisting of 150 legitimate URLs and 150 phishing URLs from openly-available research datasets. The experimental results indicate a significance performance of 100%. True Negative Rate and 0.00% False Positive Rate for legitimate instances and True Positive Rate of 96.67% with 0.03 % False Negative Rate for phishing instances which indicate that the approach offers a more efficient lightweight approach to phishing detection.

Publisher

ZIbeline International Publishing

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Phishing Detection by integrating Machine Learning and Deep Learning;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

2. Efficient Phishing Detection and Prevention Using Support Vector Machine (SVM) Algorithm;2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT);2023-04-08

3. Detecting Anomalies in Network Communities Based on Structural and Attribute Deviation;Applied Sciences;2022-11-20

4. A Deep Learning-Based Framework for Phishing Website Detection;IEEE Access;2022

5. AntiPhiMBS-Auth: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Authentication Level;Database Systems for Advanced Applications. DASFAA 2021 International Workshops;2021

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