A Non-Content Multilayers Hybrid Machine Learning Web Phishing Detection Model
Mohammad Z. Masoud, Yousef M. Jaradat, Rashed Alsakarnah · International Review on Modelling and Simulations (IREMOS) · 2022
Phishing detection is one of the most important services for cyber security. Phishing is an easy to implement attack that depends on social engineering. It is implemented leveraging web, Short Message Service (SMS) and social media. This attack is the backbone of other attacks. In this work, a new web phishing detection algorithm is proposed based on machine learning algorithms. The proposed algorithm is a hybrid machine learning algorithm that consists of two main layers. The first layer is the feature extraction and reduction layer that consists of multiple machine learning algorithms that work in parallel. The second layer consists of one feed-forward neural network layer. Different numbers and types of algorithms can be leveraged in the first layer of the proposed algorithm. The proposed model has been tested with four algorithms in the first layer, namely, K-means, support vector machine, logistic regression and random forest. Four features have been extracted from this layer and fed into the second layer “the neural network layer”. The output of the second layer shows an accuracy that exceeded 99%.