Research Article
An Empirical Analysis of Machine Learning Approaches for Phishing Detection
@INPROCEEDINGS{10.1007/978-3-031-72393-3_4, author={Ivan Cvitić and Hussam Al-Hamadi and Tibor Mijo Kuljanić and David Aleksić}, title={An Empirical Analysis of Machine Learning Approaches for Phishing Detection}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. 8th EAI International Conference, FABULOUS 2024, Zagreb, Croatia, May 9--10, 2024, Proceedings}, proceedings_a={FABULOUS}, year={2024}, month={10}, keywords={Artificial Intelligence Machine learning Deep Learning Phishing attacks}, doi={10.1007/978-3-031-72393-3_4} }- Ivan Cvitić
Hussam Al-Hamadi
Tibor Mijo Kuljanić
David Aleksić
Year: 2024
An Empirical Analysis of Machine Learning Approaches for Phishing Detection
FABULOUS
Springer
DOI: 10.1007/978-3-031-72393-3_4
Abstract
This research paper investigates the integration of Artificial Intelligence (AI), with a focus on Machine Learning (ML) and Deep Learning (DL), for bolstering cybersecurity defences against phishing attacks. Utilizing a comprehensive dataset of URL features, the study assesses the efficacy of various ML algorithms—namely Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, and K-Nearest Neighbors—in pinpointing phishing websites. Research paper is conducted using Google Colaboratory, Python libraries and Weka tool. The research identifies the Random Forest algorithm as the most effective, demonstrating superior accuracy in detecting phishing URLs during both training and testing phases. The findings accentuate the pivotal role of AI in advancing cybersecurity measures, advocating for the incorporation of sophisticated AI technologies in the fight against cyber threats. Additionally, it outlines future research directions, including the enhancement of model precision through the integration of more comprehensive data attributes. This paper significantly contributes to the cybersecurity and AI domains by showcasing the practical applications and benefits of AI in identifying and mitigating cyber risks.
