  The hypothesis here states that neural network algorithms such as Multi-layer
Perceptron (MLP) have higher accuracy in differentiating malicious and
semi-structured phishing URLs. Compared to classical machine learning
algorithms such as Logistic Regression and Multinomial Naive Bayes, the
classical algorithms rely heavily on substantial corpus data training and
machine learning experts' domain knowledge to perform complex feature
engineering. MLP could perform non-linear separable multi-classes
classification and focus less on corpus feature training. In addition,
backpropagation weight adjustment could learn which features are more important
in differentiating phishing from other attack types.
