Detection of Malicious URLs using Ensemble learning techniques

The growing risk of cyber-attacks and information vulnerability has become a major problem in today's dynamic digital environment. The necessity for strong security solutions is more critical than ever due to the development of attack techniques and the spread of social engineering strategies....

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Bibliographic Details
Published in2023 IEEE Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC) pp. 1 - 5
Main Authors Arjun, D Shashank, Samhitha, D Sai, Padmavathi, A, Hemprasanna, A
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2023
Subjects
Asia
Computer hacking
Decision Trees
Deep learning
Engineering management
Ensemble learning
Forestry
Machine learning
Phishing
Random Forest
Uniform resource locators
URL
XG boost
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Summary:The growing risk of cyber-attacks and information vulnerability has become a major problem in today's dynamic digital environment. The necessity for strong security solutions is more critical than ever due to the development of attack techniques and the spread of social engineering strategies. Hackers commonly utilize malicious Uniform Resource Locators (URLs) to trick users and launch different kinds of attacks. This research uses the power of ensemble learning to tackle this problem by mixing various machine learning techniques like XGBoost, Extra Tree Classifier, and Random Forest. This ensemble model not only performs exceptionally well at identifying hazardous URLs, but it also provides a scalable cybersecurity solution, acting as a protector against developing online threats.
DOI:10.1109/TEMSCON-ASPAC59527.2023.10531433