Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees-Reference-Cited by-同舟云学术

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Published:2024-06-28 Issue: Volume: Page:1-6
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Container-title:2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)
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Author:

Chitteti Chengamma¹,Divya Sree Tanguturi Sai²,Madhavi K. Reddy³,Pooja Pikkili²,Jayanth Sannapaneni²,Reddy Middi Harshavardhan²

Affiliation:

1. Mohan Babu University, Erstwhile Sree Vidyanikethan Engineering College(Autonomous),Dept. Of Data Science,Tirupathi,India

2. Sree Vidyanikethan Engineering College,Dept. of Information Technology,Tirupathi,India

3. Mohan Babu University,Dept. Of AI & ML,Tirupati,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10624684/10624740/10624954.pdf?arnumber=10624954

Reference20 articles.

1. Classification of Phishing Attack Solutions by Employing Deep Learning Techniques: A Systematic Literature Review

2. Intelligent web-phishing detection and protection scheme using integrated features of Images, frames and text

3. Phishing web site detection using diverse machine learning algorithms

4. An In-Depth Benchmarking and Evaluation of Phishing Detection Research for Security Needs

5. Decision Tree Classifier for Classification of Phishing Website with Info Gain Feature Selection;Shrivas;International Journal for Research in Applied Science & Engineering,2017

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