Open AccessAnalysis of Single and Ensemble Machine Learning Classifiers for Phishing Attacks Detection
Author(s) -
Akinyemi Moruff OYELAKIN,
Alimi O. M,
Mustapha I. O,
Ajiboye I. K
Publication year - 2021
Publication title -
international journal of software engineering and computer systems
Language(s) - English
Resource type - Journals
eISSN - 2289-8522
pISSN - 2180-0650
DOI - 10.15282/ijsecs.7.2.2021.5.0088
Subject(s) - random forest , phishing , ensemble learning , machine learning , computer science , artificial intelligence , random subspace method , majority rule , decision tree , precision and recall , classifier (uml) , logistic regression , support vector machine , voting , data mining , the internet , politics , world wide web , political science , law
Phishing attacks have been used in different ways to harvest the confidential information of unsuspecting internet users. To stem the tide of phishing-based attacks, several machine learning techniques have been proposed in the past. However, fewer studies have considered investigating single and ensemble machine learning-based models for the classification of phishing attacks. This study carried out performance analysis of selected single and ensemble machine learning (ML) classifiers in phishing classification. The focus is to investigate how these algorithms behave in the classification of phishing attacks in the chosen dataset. Logistic Regression and Decision Trees were chosen as single learning classifiers while simple voting techniques and Random Forest were used as the ensemble machine learning algorithms. Accuracy, Precision, Recall and F1-score were used as performance metrics. Logistic Regression algorithm recorded 0.86 as accuracy, 0.89 as precision, 0.87 as recall and 0.81 as F1-score. Similarly, the Decision Trees classifier achieved an accuracy of 0.87, 0.83 for precision, 0.88 for recall and 0.81 for F1-score. In the voting ensemble, accuracy of 0.92 was achieved. 0.90 was obtained for precision, 0.92 for recall and 0.92 for F1-score. Random Forest algorithm recorded 0.98, 0.97, 0.98 and 0.97 as accuracy, precision, recall and F1-score respectively. From the experimental analyses, Random Forest algorithm outperformed simple averaging classifier and the two single algorithms used for phishing URL detection. The study established that the ensemble techniques that were used for the experimentations are more efficient for phishing URL identification compared to the single classifiers.