An Efficient Hybrid Feature Selection Technique Toward Prediction of Suspicious URLs in IoT Environment-Reference-Cited by-同舟云学术

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An Efficient Hybrid Feature Selection Technique Toward Prediction of Suspicious URLs in IoT Environment

Published:2024 Issue: Volume:12 Page:50578-50594
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mohanty Sanjukta¹,Acharya Arup Abhinna¹,Gaber Tarek²^ORCID,Panda Namita¹,Eldesouky Esraa³,Hameed Ibrahim A.⁴^ORCID

Affiliation:

1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

2. School of Science, Engineering, and Environment, University of Salford, Salford, U.K.

3. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

4. Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Ålesund, Norway

Funder

Prince Sattam Bin Abdulaziz University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10489965.pdf?arnumber=10489965

Reference45 articles.

1. Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning

2. Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)

3. Malicious URL detection using machine learning: A survey;Sahoo;arXiv:1701.07179,2017

4. Malicious URL Detection Using Machine Learning

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