Abstract
Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.
Highlights
Cybercrimes became a concern of many organizations and scholars in the current years
A legitimate websites (LWs) of a selected organization is faked by the attacker and distributed to victims via fake or junk emails or posted URLs in social media and networks, or any medium of communication
The default configuration classifier performance is shown in Tabs. 2–5 to obtain the highest precision compared with other classifiers
Summary
Cybercrimes became a concern of many organizations and scholars in the current years. The attackers stole the user's credentials and information by using false emails or websites that look like original ones This type of attack became a concern because it affects many internet users and organizations. A LW of a selected organization is faked by the attacker and distributed to victims via fake or junk emails or posted URLs in social media and networks, or any medium of communication. This may lead victims to click on the links in those emails or posts which will redirect them to the fake website [1]
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