Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification
This work addresses phishing URL detection, which is a domain-specific cybersecurity issue, but it appears incremental as it applies an existing neural network method to this task without major innovations.
The paper tackles the problem of detecting malicious phishing URLs by proposing a Multi-Layer Perceptron (MLP) neural network, which achieves higher accuracy compared to classical machine learning algorithms like Logistic Regression and Multinomial Naive Bayes, though no concrete numbers are provided.
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.