网络钓鱼
计算机科学
决策树
机器学习
支持向量机
朴素贝叶斯分类器
人工智能
Boosting(机器学习)
集成学习
阿达布思
数据挖掘
多数决原则
随机森林
统计分类
互联网
万维网
作者
Prakash Pathak,Akhilesh Kumar Shrivas
标识
DOI:10.1109/otcon56053.2023.10113909
摘要
Phishing is the process of trying to get sensitive data from unauthorized persons, such as usernames, passwords, credit card numbers, and debit card information. Since there is no one method to properly reduce every vulnerability due to the problem of phishing. There are many techniques are frequently utilized to reduce specific attacks. The main aim of this research work is to develop a robust machine learning based model that can detect and classify URL based phishing data. This paper proposed a new ensemble model that is a combination of Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR) using the voting scheme ensemble technique. The proposed ensemble model compared with other individual classifiers like Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR) techniques, and ensemble classifiers like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost) where our proposed ensemble model achieved remarkable performance of 99.02% accuracy with 10-fold cross validation technique. Finally, machine learning based classification techniques are suitable and robust methods that handle the dynamic nature of phishing techniques and provide an accurate method of classification.
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