MOE/RF: A Novel Phishing Detection Model Based on Revised Multiobjective Evolution Optimization Algorithm and Random Forest

To effectively boost computer usage, machine learning models are used in several phishing detection systems to classify enormous phishing datasets. Based on phishing patterns, researchers prefer to extract a considerable number of features to improve phishing detection performance. However, redundan...

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Bibliographic Details
Published in:IEEE eTransactions on network and service management Vol. 19; no. 4; pp. 4461 - 4478
Main Authors: Zhu, Erzhou, Chen, Zhile, Cui, Jie, Zhong, Hong
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Blocklists
Datasets
evolution computation
Evolutionary algorithms
Feature extraction
Machine learning
Model accuracy
multi-objective optimization
Multiple objective analysis
Optimization
Optimization algorithms
Performance degradation
Performance enhancement
Phishing
Phishing detection
random forest
Sociology
Statistics
Target detection
Visualization
Websites
ISSN:1932-4537, 1932-4537
Online Access:Get full text
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Summary:To effectively boost computer usage, machine learning models are used in several phishing detection systems to classify enormous phishing datasets. Based on phishing patterns, researchers prefer to extract a considerable number of features to improve phishing detection performance. However, redundant and useless features in the feature set degrade the performance of the underlying classification models. In addition, several existing phishing detection models mainly focus on detection accuracy and overlook recall rates. However, in phishing detection, it is more harmful to falsely detect a phishing website as a legitimate website than it is to detect a legitimate website as a phishing website. This study proposes a novel phishing detection model, multi-objective evolution/random forest (MOE/RF), which is based on the revised multi-objective evolution optimization algorithm (MOE) and random forest (RF). The MOE/RF model uses accuracy as the detection target and minimizes the probability of false detection of phishing sites. In addition, two new strategies, the symmetric uncertainty-based population initialization and the population state-based adaptive environmental selection, are proposed to improve the performance of the MOE. Experimental results on testing five different phishing datasets demonstrated that the MOE/RF performs superior to several existing methods.
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ISSN:1932-4537
1932-4537
DOI:10.1109/TNSM.2022.3162885